diff --git a/main/_images/RReLU.png b/main/_images/RReLU.png
index 47e7582554e..c3507189a42 100644
Binary files a/main/_images/RReLU.png and b/main/_images/RReLU.png differ
diff --git a/main/_modules/index.html b/main/_modules/index.html
index a8d3b4ad5cd..f37060da62c 100644
--- a/main/_modules/index.html
+++ b/main/_modules/index.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch.html b/main/_modules/torch.html
index af5d910bbed..f121ebacc7c 100644
--- a/main/_modules/torch.html
+++ b/main/_modules/torch.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/__config__.html b/main/_modules/torch/__config__.html
index 9d061d0852a..04cd6b11d50 100644
--- a/main/_modules/torch/__config__.html
+++ b/main/_modules/torch/__config__.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/__future__.html b/main/_modules/torch/__future__.html
index 07a81c294f0..2051930523e 100644
--- a/main/_modules/torch/__future__.html
+++ b/main/_modules/torch/__future__.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/_jit_internal.html b/main/_modules/torch/_jit_internal.html
index 7f022b15e3c..dfdb65fbdb3 100644
--- a/main/_modules/torch/_jit_internal.html
+++ b/main/_modules/torch/_jit_internal.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -628,7 +628,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 <span class="kn">import</span> <span class="nn">typing</span>
 <span class="kn">import</span> <span class="nn">warnings</span>
 <span class="kn">import</span> <span class="nn">weakref</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="p">(</span>  <span class="c1"># noqa: F401  # (Dict, List, Tuple) imported by torch.jit.annotations</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="p">(</span>  <span class="c1"># noqa: UP035, F401  # (Dict, List, Tuple) imported by torch.jit.annotations</span>
     <span class="n">Any</span><span class="p">,</span>
     <span class="n">Callable</span><span class="p">,</span>
     <span class="n">Dict</span><span class="p">,</span>
@@ -1733,7 +1733,8 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">is_tuple</span><span class="p">(</span><span class="n">ann</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-    <span class="k">if</span> <span class="n">ann</span> <span class="ow">is</span> <span class="n">Tuple</span><span class="p">:</span>
+    <span class="c1"># Check for typing.Tuple missing args (but `tuple` is fine)</span>
+    <span class="k">if</span> <span class="n">ann</span> <span class="ow">is</span> <span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span><span class="p">:</span>  <span class="c1"># noqa: UP006</span>
         <span class="n">raise_error_container_parameter_missing</span><span class="p">(</span><span class="s2">&quot;Tuple&quot;</span><span class="p">)</span>
 
     <span class="c1"># For some reason Python 3.7 violates the Type[A, B].__origin__ == Type rule</span>
@@ -1741,35 +1742,31 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="kc">False</span>
 
     <span class="n">ann_origin</span> <span class="o">=</span> <span class="n">get_origin</span><span class="p">(</span><span class="n">ann</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">IS_PY39_PLUS</span> <span class="ow">and</span> <span class="n">ann</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">==</span> <span class="s2">&quot;builtins&quot;</span> <span class="ow">and</span> <span class="n">ann_origin</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">True</span>
-    <span class="k">return</span> <span class="n">ann</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">==</span> <span class="s2">&quot;typing&quot;</span> <span class="ow">and</span> <span class="p">(</span><span class="n">ann_origin</span> <span class="ow">is</span> <span class="n">Tuple</span> <span class="ow">or</span> <span class="n">ann_origin</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ann</span><span class="o">.</span><span class="vm">__module__</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;builtins&quot;</span><span class="p">,</span> <span class="s2">&quot;typing&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">ann_origin</span> <span class="ow">is</span> <span class="nb">tuple</span>
 
 
 <span class="k">def</span> <span class="nf">is_list</span><span class="p">(</span><span class="n">ann</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-    <span class="k">if</span> <span class="n">ann</span> <span class="ow">is</span> <span class="n">List</span><span class="p">:</span>
+    <span class="c1"># Check for typing.List missing args (but `list` is fine)</span>
+    <span class="k">if</span> <span class="n">ann</span> <span class="ow">is</span> <span class="n">typing</span><span class="o">.</span><span class="n">List</span><span class="p">:</span>  <span class="c1"># noqa: UP006</span>
         <span class="n">raise_error_container_parameter_missing</span><span class="p">(</span><span class="s2">&quot;List&quot;</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ann</span><span class="p">,</span> <span class="s2">&quot;__module__&quot;</span><span class="p">):</span>
         <span class="k">return</span> <span class="kc">False</span>
 
     <span class="n">ann_origin</span> <span class="o">=</span> <span class="n">get_origin</span><span class="p">(</span><span class="n">ann</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">IS_PY39_PLUS</span> <span class="ow">and</span> <span class="n">ann</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">==</span> <span class="s2">&quot;builtins&quot;</span> <span class="ow">and</span> <span class="n">ann_origin</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">True</span>
-    <span class="k">return</span> <span class="n">ann</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">==</span> <span class="s2">&quot;typing&quot;</span> <span class="ow">and</span> <span class="p">(</span><span class="n">ann_origin</span> <span class="ow">is</span> <span class="n">List</span> <span class="ow">or</span> <span class="n">ann_origin</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ann</span><span class="o">.</span><span class="vm">__module__</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;builtins&quot;</span><span class="p">,</span> <span class="s2">&quot;typing&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">ann_origin</span> <span class="ow">is</span> <span class="nb">list</span>
 
 
 <span class="k">def</span> <span class="nf">is_dict</span><span class="p">(</span><span class="n">ann</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-    <span class="k">if</span> <span class="n">ann</span> <span class="ow">is</span> <span class="n">Dict</span><span class="p">:</span>
+    <span class="c1"># Check for typing.Dict missing args (but `dict` is fine)</span>
+    <span class="k">if</span> <span class="n">ann</span> <span class="ow">is</span> <span class="n">typing</span><span class="o">.</span><span class="n">Dict</span><span class="p">:</span>  <span class="c1"># noqa: UP006</span>
         <span class="n">raise_error_container_parameter_missing</span><span class="p">(</span><span class="s2">&quot;Dict&quot;</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ann</span><span class="p">,</span> <span class="s2">&quot;__module__&quot;</span><span class="p">):</span>
         <span class="k">return</span> <span class="kc">False</span>
 
     <span class="n">ann_origin</span> <span class="o">=</span> <span class="n">get_origin</span><span class="p">(</span><span class="n">ann</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">IS_PY39_PLUS</span> <span class="ow">and</span> <span class="n">ann</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">==</span> <span class="s2">&quot;builtins&quot;</span> <span class="ow">and</span> <span class="n">ann_origin</span> <span class="ow">is</span> <span class="nb">dict</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">True</span>
-    <span class="k">return</span> <span class="n">ann</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">==</span> <span class="s2">&quot;typing&quot;</span> <span class="ow">and</span> <span class="p">(</span><span class="n">ann_origin</span> <span class="ow">is</span> <span class="n">Dict</span> <span class="ow">or</span> <span class="n">ann_origin</span> <span class="ow">is</span> <span class="nb">dict</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ann</span><span class="o">.</span><span class="vm">__module__</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;builtins&quot;</span><span class="p">,</span> <span class="s2">&quot;typing&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">ann_origin</span> <span class="ow">is</span> <span class="nb">dict</span>
 
 
 <span class="k">def</span> <span class="nf">is_union</span><span class="p">(</span><span class="n">ann</span><span class="p">):</span>
@@ -1979,11 +1976,11 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">check_args_exist</span><span class="p">(</span><span class="n">target_type</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-    <span class="k">if</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="n">List</span> <span class="ow">or</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="n">typing</span><span class="o">.</span><span class="n">List</span> <span class="ow">or</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="nb">list</span><span class="p">:</span>  <span class="c1"># noqa: UP006</span>
         <span class="n">raise_error_container_parameter_missing</span><span class="p">(</span><span class="s2">&quot;List&quot;</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="n">Tuple</span> <span class="ow">or</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
+    <span class="k">elif</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span> <span class="ow">or</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>  <span class="c1"># noqa: UP006</span>
         <span class="n">raise_error_container_parameter_missing</span><span class="p">(</span><span class="s2">&quot;Tuple&quot;</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="n">Dict</span> <span class="ow">or</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="nb">dict</span><span class="p">:</span>
+    <span class="k">elif</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="n">typing</span><span class="o">.</span><span class="n">Dict</span> <span class="ow">or</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="nb">dict</span><span class="p">:</span>  <span class="c1"># noqa: UP006</span>
         <span class="n">raise_error_container_parameter_missing</span><span class="p">(</span><span class="s2">&quot;Dict&quot;</span><span class="p">)</span>
     <span class="k">elif</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">target_type</span> <span class="ow">is</span> <span class="n">Optional</span><span class="p">:</span>
         <span class="n">raise_error_container_parameter_missing</span><span class="p">(</span><span class="s2">&quot;Optional&quot;</span><span class="p">)</span>
@@ -2007,7 +2004,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
     <span class="n">check_args_exist</span><span class="p">(</span><span class="n">target_type</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">return</span> <span class="kc">False</span>
-    <span class="k">elif</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="nb">list</span> <span class="ow">or</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="n">List</span><span class="p">:</span>
+    <span class="k">elif</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="nb">list</span> <span class="ow">or</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="n">typing</span><span class="o">.</span><span class="n">List</span><span class="p">:</span>  <span class="c1"># noqa: UP006</span>
         <span class="n">check_empty_containers</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
             <span class="k">return</span> <span class="kc">False</span>
@@ -2021,7 +2018,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
             <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">el</span><span class="p">,</span> <span class="n">arg_type</span><span class="p">):</span>
                 <span class="k">return</span> <span class="kc">False</span>
         <span class="k">return</span> <span class="kc">True</span>
-    <span class="k">elif</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="n">Dict</span> <span class="ow">or</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="nb">dict</span><span class="p">:</span>
+    <span class="k">elif</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="n">typing</span><span class="o">.</span><span class="n">Dict</span> <span class="ow">or</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="nb">dict</span><span class="p">:</span>  <span class="c1"># noqa: UP006</span>
         <span class="n">check_empty_containers</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
             <span class="k">return</span> <span class="kc">False</span>
@@ -2038,7 +2035,7 @@ <h1>Source code for torch._jit_internal</h1><div class="highlight"><pre>
             <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">val_type</span><span class="p">):</span>
                 <span class="k">return</span> <span class="kc">False</span>
         <span class="k">return</span> <span class="kc">True</span>
-    <span class="k">elif</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="n">Tuple</span> <span class="ow">or</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
+    <span class="k">elif</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="n">typing</span><span class="o">.</span><span class="n">Tuple</span> <span class="ow">or</span> <span class="n">origin_type</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>  <span class="c1"># noqa: UP006</span>
         <span class="n">check_empty_containers</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
             <span class="k">return</span> <span class="kc">False</span>
diff --git a/main/_modules/torch/_library/custom_ops.html b/main/_modules/torch/_library/custom_ops.html
index d6955b2fbb1..d08eec51f1f 100644
--- a/main/_modules/torch/_library/custom_ops.html
+++ b/main/_modules/torch/_library/custom_ops.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/_lobpcg.html b/main/_modules/torch/_lobpcg.html
index 7f6e2b3b787..40a9562a4c2 100644
--- a/main/_modules/torch/_lobpcg.html
+++ b/main/_modules/torch/_lobpcg.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/_logging/_internal.html b/main/_modules/torch/_logging/_internal.html
index 4faedba9099..37ce8b2dc82 100644
--- a/main/_modules/torch/_logging/_internal.html
+++ b/main/_modules/torch/_logging/_internal.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/_lowrank.html b/main/_modules/torch/_lowrank.html
index f739b61a6c2..e8e999b837d 100644
--- a/main/_modules/torch/_lowrank.html
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@@ -2109,7 +2109,7 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
 <span class="sd">        Returns the uniquely determined tuple of int describing the dim order or</span>
 <span class="sd">        physical layout of :attr:`self`.</span>
 
-<span class="sd">        The dim order represents how dimensions are laid out in memory,</span>
+<span class="sd">        The dim order represents how dimensions are laid out in memory of dense tensors,</span>
 <span class="sd">        starting from the outermost to the innermost dimension.</span>
 
 <span class="sd">        Note that the dim order may not always be uniquely determined.</span>
@@ -2150,6 +2150,12 @@ <h1>Source code for torch._tensor</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">has_torch_function_unary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
             <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">dim_order</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="p">,),</span> <span class="bp">self</span><span class="p">)</span>
 
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_sparse</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Can&#39;t get dim order on sparse type: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">()</span><span class="si">}</span><span class="s2"> &quot;</span>
+                <span class="s2">&quot;Use Tensor.to_dense() to convert to a dense tensor first.&quot;</span>
+            <span class="p">)</span>
+
         <span class="c1"># Sanity check ambiguity_check data types</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ambiguity_check</span><span class="p">,</span> <span class="nb">bool</span><span class="p">):</span>
             <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ambiguity_check</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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index 653bdec50a6..a287b8c1adc 100644
--- a/main/_modules/torch/distributed/checkpoint/format_utils.html
+++ b/main/_modules/torch/distributed/checkpoint/format_utils.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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index f356dd6b367..c05421a4c8b 100644
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index d7e1093c000..0136bbef648 100644
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diff --git a/main/_modules/torch/distributed/checkpoint/state_dict.html b/main/_modules/torch/distributed/checkpoint/state_dict.html
index 3bb14fbb920..c3663d6eccb 100644
--- a/main/_modules/torch/distributed/checkpoint/state_dict.html
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@@ -299,7 +299,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/checkpoint/state_dict_loader.html b/main/_modules/torch/distributed/checkpoint/state_dict_loader.html
index 6c1b6d845a7..14e7e7582d2 100644
--- a/main/_modules/torch/distributed/checkpoint/state_dict_loader.html
+++ b/main/_modules/torch/distributed/checkpoint/state_dict_loader.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -667,6 +667,7 @@ <h1>Source code for torch.distributed.checkpoint.state_dict_loader</h1><div clas
     <span class="n">storage_reader</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">StorageReader</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">planner</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">LoadPlanner</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">process_group</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">dist</span><span class="o">.</span><span class="n">ProcessGroup</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">no_dist</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Load a distributed ``state_dict`` in SPMD style.</span>
@@ -719,7 +720,8 @@ <h1>Source code for torch.distributed.checkpoint.state_dict_loader</h1><div clas
 <span class="sd">        process_group (Optional[ProcessGroup]):</span>
 <span class="sd">            ProcessGroup to be used for cross-rank synchronization.</span>
 <span class="sd">            (Default: ``None``)</span>
-
+<span class="sd">        no_dist (bool): If ``True``, this function will assume the intent is to load</span>
+<span class="sd">            a checkpoint without using cross-rank synchronization. (Default: ``False``)</span>
 <span class="sd">    Returns:</span>
 <span class="sd">        None.</span>
 
@@ -749,10 +751,10 @@ <h1>Source code for torch.distributed.checkpoint.state_dict_loader</h1><div clas
 <span class="sd">        rank has an individual GPU, via ``torch.cuda.set_device()``.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="n">no_dist</span> <span class="o">=</span> <span class="ow">not</span> <span class="p">(</span><span class="n">dist</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="ow">and</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_initialized</span><span class="p">())</span>
+    <span class="n">no_dist</span> <span class="o">=</span> <span class="n">no_dist</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_available</span><span class="p">())</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_initialized</span><span class="p">())</span>
     <span class="k">if</span> <span class="n">no_dist</span><span class="p">:</span>
         <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-            <span class="s2">&quot;torch.distributed is unavailable or uninitialized, assuming the intent is to load in a single process.&quot;</span>
+            <span class="s2">&quot;torch.distributed is disabled, unavailable or uninitialized, assuming the intent is to load in a single process.&quot;</span>
         <span class="p">)</span>
 
     <span class="k">with</span> <span class="n">_profile</span><span class="p">():</span>
diff --git a/main/_modules/torch/distributed/checkpoint/state_dict_saver.html b/main/_modules/torch/distributed/checkpoint/state_dict_saver.html
index d7f613b97ad..648429fc31d 100644
--- a/main/_modules/torch/distributed/checkpoint/state_dict_saver.html
+++ b/main/_modules/torch/distributed/checkpoint/state_dict_saver.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -673,6 +673,7 @@ <h1>Source code for torch.distributed.checkpoint.state_dict_saver</h1><div class
     <span class="n">storage_writer</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">StorageWriter</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">planner</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">SavePlanner</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">process_group</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">dist</span><span class="o">.</span><span class="n">ProcessGroup</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">no_dist</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Metadata</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Save a distributed model in SPMD style.</span>
@@ -722,6 +723,10 @@ <h1>Source code for torch.distributed.checkpoint.state_dict_saver</h1><div class
 <span class="sd">        process_group (Optional[ProcessGroup]):</span>
 <span class="sd">            ProcessGroup to be used for cross-rank synchronization.</span>
 <span class="sd">            (Default: ``None``)</span>
+<span class="sd">        no_dist (bool):</span>
+<span class="sd">            If ``True``, this function will assume the intent is to load</span>
+<span class="sd">            a checkpoint without using cross-rank synchronization.</span>
+<span class="sd">            (Default: ``False``)</span>
 
 <span class="sd">    Returns:</span>
 <span class="sd">        Metadata: Metadata object for the saved checkpoint.</span>
@@ -748,10 +753,10 @@ <h1>Source code for torch.distributed.checkpoint.state_dict_saver</h1><div class
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_log_api_usage_once</span><span class="p">(</span><span class="s2">&quot;torch.distributed.checkpoint.save&quot;</span><span class="p">)</span>
 
-    <span class="n">no_dist</span> <span class="o">=</span> <span class="ow">not</span> <span class="p">(</span><span class="n">dist</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="ow">and</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_initialized</span><span class="p">())</span>
+    <span class="n">no_dist</span> <span class="o">=</span> <span class="n">no_dist</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_available</span><span class="p">())</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_initialized</span><span class="p">())</span>
     <span class="k">if</span> <span class="n">no_dist</span><span class="p">:</span>
         <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-            <span class="s2">&quot;torch.distributed is unavailable or uninitialized, assuming the intent is to save in a single process.&quot;</span>
+            <span class="s2">&quot;torch.distributed is disabled, unavailable or uninitialized, assuming the intent is to save in a single process.&quot;</span>
         <span class="p">)</span>
 
     <span class="k">with</span> <span class="n">_profile</span><span class="p">():</span>
diff --git a/main/_modules/torch/distributed/checkpoint/stateful.html b/main/_modules/torch/distributed/checkpoint/stateful.html
index 15b445fd685..03708d0e99b 100644
--- a/main/_modules/torch/distributed/checkpoint/stateful.html
+++ b/main/_modules/torch/distributed/checkpoint/stateful.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/checkpoint/storage.html b/main/_modules/torch/distributed/checkpoint/storage.html
index 4480b4f6e26..8695a90a781 100644
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@@ -299,7 +299,7 @@
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index ba7581b3598..e799e65733a 100644
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@@ -299,7 +299,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/distributed_c10d.html b/main/_modules/torch/distributed/distributed_c10d.html
index 9a4b5b8cc19..7ba59fc5e8d 100644
--- a/main/_modules/torch/distributed/distributed_c10d.html
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@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/elastic/agent/server/api.html b/main/_modules/torch/distributed/elastic/agent/server/api.html
index baed7f40105..b56bdf7f1b9 100644
--- a/main/_modules/torch/distributed/elastic/agent/server/api.html
+++ b/main/_modules/torch/distributed/elastic/agent/server/api.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/elastic/agent/server/health_check_server.html b/main/_modules/torch/distributed/elastic/agent/server/health_check_server.html
index c083ae09515..bd55f3a6ae3 100644
--- a/main/_modules/torch/distributed/elastic/agent/server/health_check_server.html
+++ b/main/_modules/torch/distributed/elastic/agent/server/health_check_server.html
@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/elastic/agent/server/local_elastic_agent.html b/main/_modules/torch/distributed/elastic/agent/server/local_elastic_agent.html
index d1d5b66f44f..35a85583077 100644
--- a/main/_modules/torch/distributed/elastic/agent/server/local_elastic_agent.html
+++ b/main/_modules/torch/distributed/elastic/agent/server/local_elastic_agent.html
@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/elastic/control_plane.html b/main/_modules/torch/distributed/elastic/control_plane.html
index 04d451ddc48..cf428de129b 100644
--- a/main/_modules/torch/distributed/elastic/control_plane.html
+++ b/main/_modules/torch/distributed/elastic/control_plane.html
@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/elastic/events.html b/main/_modules/torch/distributed/elastic/events.html
index f028605ae92..6d418616770 100644
--- a/main/_modules/torch/distributed/elastic/events.html
+++ b/main/_modules/torch/distributed/elastic/events.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/distributed/elastic/events/api.html b/main/_modules/torch/distributed/elastic/events/api.html
index 72fce020ed9..4af60e97535 100644
--- a/main/_modules/torch/distributed/elastic/events/api.html
+++ b/main/_modules/torch/distributed/elastic/events/api.html
@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/elastic/events/handlers.html b/main/_modules/torch/distributed/elastic/events/handlers.html
index f84dd84dabb..d527b16d38d 100644
--- a/main/_modules/torch/distributed/elastic/events/handlers.html
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diff --git a/main/_modules/torch/distributed/elastic/metrics/api.html b/main/_modules/torch/distributed/elastic/metrics/api.html
index 5fdca1f015d..7b038af5c04 100644
--- a/main/_modules/torch/distributed/elastic/metrics/api.html
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diff --git a/main/_modules/torch/distributed/elastic/multiprocessing.html b/main/_modules/torch/distributed/elastic/multiprocessing.html
index 575ae941e66..31e9ca2c1be 100644
--- a/main/_modules/torch/distributed/elastic/multiprocessing.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/_modules/torch/distributed/elastic/multiprocessing/api.html b/main/_modules/torch/distributed/elastic/multiprocessing/api.html
index 8fb8117020b..3a80e1f36e6 100644
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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index d4ac29f3b61..d8b37704c9f 100644
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+++ b/main/_modules/torch/export/exported_program.html
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diff --git a/main/_modules/torch/export/graph_signature.html b/main/_modules/torch/export/graph_signature.html
index 0868f90e8eb..0adf62c48eb 100644
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+++ b/main/_modules/torch/export/graph_signature.html
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diff --git a/main/_modules/torch/export/passes.html b/main/_modules/torch/export/passes.html
index 08ebc44ecfc..416a3ce6ff8 100644
--- a/main/_modules/torch/export/passes.html
+++ b/main/_modules/torch/export/passes.html
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diff --git a/main/_modules/torch/export/unflatten.html b/main/_modules/torch/export/unflatten.html
index a53011bed6e..bb4950abe4b 100644
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+++ b/main/_modules/torch/export/unflatten.html
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           <div class="pytorch-left-menu-search">
             
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diff --git a/main/_modules/torch/func.html b/main/_modules/torch/func.html
index 97208f57a09..413c4ca5721 100644
--- a/main/_modules/torch/func.html
+++ b/main/_modules/torch/func.html
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           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/_modules/torch/functional.html b/main/_modules/torch/functional.html
index c396a9f0988..d98bbcac46c 100644
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+++ b/main/_modules/torch/functional.html
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diff --git a/main/_modules/torch/futures.html b/main/_modules/torch/futures.html
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diff --git a/main/_modules/torch/fx/_symbolic_trace.html b/main/_modules/torch/fx/_symbolic_trace.html
index 4b662e50a22..d22330d29fc 100644
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+++ b/main/_modules/torch/fx/_symbolic_trace.html
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           <div class="pytorch-left-menu-search">
             
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diff --git a/main/_modules/torch/fx/experimental/proxy_tensor.html b/main/_modules/torch/fx/experimental/proxy_tensor.html
index c58cca83a56..6e36d7b59f0 100644
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+++ b/main/_modules/torch/fx/experimental/proxy_tensor.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -660,11 +660,7 @@ <h1>Source code for torch.fx.experimental.proxy_tensor</h1><div class="highlight
 <span class="kn">from</span> <span class="nn">torch._subclasses.meta_utils</span> <span class="kn">import</span> <span class="n">is_sparse_any</span>
 <span class="kn">from</span> <span class="nn">torch.fx</span> <span class="kn">import</span> <span class="n">GraphModule</span><span class="p">,</span> <span class="n">Proxy</span><span class="p">,</span> <span class="n">Tracer</span>
 <span class="kn">from</span> <span class="nn">torch.fx.graph_module</span> <span class="kn">import</span> <span class="n">_assign_attr</span>
-<span class="kn">from</span> <span class="nn">torch.fx.node</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">_side_effectful_need_to_be_preserved_pre_dispatch</span><span class="p">,</span>
-    <span class="n">Argument</span><span class="p">,</span>
-    <span class="n">Target</span><span class="p">,</span>
-<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">torch.fx.node</span> <span class="kn">import</span> <span class="n">_side_effectful_need_to_be_preserved_pre_dispatch</span>
 <span class="kn">from</span> <span class="nn">torch.fx.passes.shape_prop</span> <span class="kn">import</span> <span class="n">_extract_tensor_metadata</span>
 <span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">Module</span>
 <span class="kn">from</span> <span class="nn">torch.overrides</span> <span class="kn">import</span> <span class="n">TorchFunctionMode</span>
@@ -1699,40 +1695,6 @@ <h1>Source code for torch.fx.experimental.proxy_tensor</h1><div class="highlight
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">e</span>
 
-    <span class="k">def</span> <span class="nf">create_node</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">kind</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-        <span class="n">target</span><span class="p">:</span> <span class="n">Target</span><span class="p">,</span>
-        <span class="n">args</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
-        <span class="n">kwargs</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Argument</span><span class="p">],</span>
-        <span class="n">name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">type_expr</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">Node</span><span class="p">:</span>
-        <span class="n">node</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">create_node</span><span class="p">(</span><span class="n">kind</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">type_expr</span><span class="p">)</span>  <span class="c1"># type: ignore[arg-type]</span>
-
-        <span class="k">def</span> <span class="nf">map_fn</span><span class="p">(</span><span class="n">v</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_ExtractValType</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">Node</span><span class="p">)</span> <span class="ow">or</span> <span class="s2">&quot;val&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">meta</span><span class="p">:</span>
-                <span class="k">return</span> <span class="kc">None</span>
-            <span class="n">val</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span>
-            <span class="c1"># other subclasses like FunctionalTensor error on `extract_val`</span>
-            <span class="c1"># &quot;Attempting to use FunctionalTensor on its own.&quot; just store FakeTensors for now</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">FakeTensor</span><span class="p">):</span>
-                <span class="k">return</span> <span class="kc">None</span>
-            <span class="k">return</span> <span class="n">extract_val</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">])</span>
-
-        <span class="c1"># TODO: opt-in mechanism ?</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
-            <span class="n">target</span><span class="p">,</span>
-            <span class="p">(</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">_higher_order_ops</span><span class="o">.</span><span class="n">triton_kernel_wrap</span><span class="o">.</span><span class="n">TritonKernelWrapperFunctional</span><span class="p">,</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">_higher_order_ops</span><span class="o">.</span><span class="n">triton_kernel_wrap</span><span class="o">.</span><span class="n">TritonKernelWrapperMutation</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">):</span>
-            <span class="n">arg_inp</span><span class="p">,</span> <span class="n">kwarg_inp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">map_aggregate</span><span class="p">((</span><span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">),</span> <span class="n">map_fn</span><span class="p">)</span>  <span class="c1"># type: ignore[misc, arg-type]</span>
-            <span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;arg_kwarg_vals&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">arg_inp</span><span class="p">,</span> <span class="n">kwarg_inp</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">node</span>
-
 
 <span class="k">def</span> <span class="nf">_make_temp_remove_mode_context_manager</span><span class="p">(</span>
     <span class="n">mode_ty</span><span class="p">:</span> <span class="nb">type</span><span class="p">[</span><span class="n">TorchFunctionMode</span><span class="p">],</span>
diff --git a/main/_modules/torch/fx/experimental/symbolic_shapes.html b/main/_modules/torch/fx/experimental/symbolic_shapes.html
index 42016f95574..d826f5df947 100644
--- a/main/_modules/torch/fx/experimental/symbolic_shapes.html
+++ b/main/_modules/torch/fx/experimental/symbolic_shapes.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/fx/graph.html b/main/_modules/torch/fx/graph.html
index 8d4a08d4053..c3bad0e529d 100644
--- a/main/_modules/torch/fx/graph.html
+++ b/main/_modules/torch/fx/graph.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/fx/graph_module.html b/main/_modules/torch/fx/graph_module.html
index a2f4e055f90..4541c9f2d89 100644
--- a/main/_modules/torch/fx/graph_module.html
+++ b/main/_modules/torch/fx/graph_module.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/fx/interpreter.html b/main/_modules/torch/fx/interpreter.html
index 6fb4ceac8a2..898b2e136c9 100644
--- a/main/_modules/torch/fx/interpreter.html
+++ b/main/_modules/torch/fx/interpreter.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/fx/node.html b/main/_modules/torch/fx/node.html
index ae9b759bada..ce3330aab5e 100644
--- a/main/_modules/torch/fx/node.html
+++ b/main/_modules/torch/fx/node.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/fx/proxy.html b/main/_modules/torch/fx/proxy.html
index 4828a4ecf67..488428211df 100644
--- a/main/_modules/torch/fx/proxy.html
+++ b/main/_modules/torch/fx/proxy.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -722,7 +722,6 @@ <h1>Source code for torch.fx.proxy</h1><div class="highlight"><pre>
     <span class="s2">&quot;_numeric_debug_handle&quot;</span><span class="p">,</span>  <span class="c1"># TODO deprecated</span>
     <span class="s2">&quot;custom&quot;</span><span class="p">,</span>
     <span class="s2">&quot;partitioner_tag&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;arg_kwarg_vals&quot;</span><span class="p">,</span>
 <span class="p">]</span>
 
 
diff --git a/main/_modules/torch/fx/subgraph_rewriter.html b/main/_modules/torch/fx/subgraph_rewriter.html
index 61cb18c519c..6195bb452dc 100644
--- a/main/_modules/torch/fx/subgraph_rewriter.html
+++ b/main/_modules/torch/fx/subgraph_rewriter.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/hub.html b/main/_modules/torch/hub.html
index 8015a2f21d6..1f08e67bc05 100644
--- a/main/_modules/torch/hub.html
+++ b/main/_modules/torch/hub.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/jit.html b/main/_modules/torch/jit.html
index 52321f5a340..57b4d3ebe5b 100644
--- a/main/_modules/torch/jit.html
+++ b/main/_modules/torch/jit.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/jit/_async.html b/main/_modules/torch/jit/_async.html
index c0ab1e75c9f..4982d5a46a1 100644
--- a/main/_modules/torch/jit/_async.html
+++ b/main/_modules/torch/jit/_async.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -771,7 +771,7 @@ <h1>Source code for torch.nn.attention.flex_attention</h1><div class="highlight"
         <span class="n">valid_indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">index_mask</span><span class="p">,</span> <span class="n">kv_indices</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">)</span>
 
         <span class="c1"># set the values in &#39;a&#39; to 1 where the indices are valid</span>
-        <span class="n">dense_mask</span><span class="p">[</span><span class="n">row_indices</span><span class="p">,</span> <span class="n">valid_indices</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">dense_mask</span><span class="p">[</span><span class="n">row_indices</span><span class="p">,</span> <span class="n">valid_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">dense_mask</span><span class="o">.</span><span class="n">new_ones</span><span class="p">(())</span>
         <span class="k">return</span> <span class="n">dense_mask</span><span class="p">[:,</span> <span class="p">:</span><span class="n">num_cols</span><span class="p">]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
 
     <span class="n">create_dense_batched</span> <span class="o">=</span> <span class="n">create_dense_one</span>
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diff --git a/main/_modules/torch/nn/modules/conv.html b/main/_modules/torch/nn/modules/conv.html
index 2ba5d6e793f..30c7164d4d8 100644
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+++ b/main/_modules/torch/nn/modules/conv.html
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@@ -608,7 +608,7 @@
   <h1>Source code for torch.nn.modules.conv</h1><div class="highlight"><pre>
 <span></span><span class="c1"># mypy: allow-untyped-defs</span>
 <span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
 <span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">deprecated</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
@@ -1396,8 +1396,8 @@ <h1>Source code for torch.nn.modules.conv</h1><div class="highlight"><pre>
                     <span class="sa">f</span><span class="s2">&quot;or </span><span class="si">{</span><span class="n">num_non_spatial_dims</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">num_spatial_dims</span><span class="si">}</span><span class="s2"> elements (got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">output_size</span><span class="p">)</span><span class="si">}</span><span class="s2">)&quot;</span>
                 <span class="p">)</span>
 
-            <span class="n">min_sizes</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="p">[])</span>
-            <span class="n">max_sizes</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="p">[])</span>
+            <span class="n">min_sizes</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="p">[])</span>
+            <span class="n">max_sizes</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="p">[])</span>
             <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_spatial_dims</span><span class="p">):</span>
                 <span class="n">dim_size</span> <span class="o">=</span> <span class="p">(</span>
                     <span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">d</span> <span class="o">+</span> <span class="n">num_non_spatial_dims</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">stride</span><span class="p">[</span><span class="n">d</span><span class="p">]</span>
@@ -1419,7 +1419,7 @@ <h1>Source code for torch.nn.modules.conv</h1><div class="highlight"><pre>
                         <span class="sa">f</span><span class="s2">&quot;from </span><span class="si">{</span><span class="n">min_sizes</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">max_sizes</span><span class="si">}</span><span class="s2"> (for an input of </span><span class="si">{</span><span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">:]</span><span class="si">}</span><span class="s2">)&quot;</span>
                     <span class="p">)</span>
 
-            <span class="n">res</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="p">[])</span>
+            <span class="n">res</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="p">[])</span>
             <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_spatial_dims</span><span class="p">):</span>
                 <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output_size</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o">-</span> <span class="n">min_sizes</span><span class="p">[</span><span class="n">d</span><span class="p">])</span>
 
diff --git a/main/_modules/torch/nn/modules/distance.html b/main/_modules/torch/nn/modules/distance.html
index e096b542104..09d40d607f1 100644
--- a/main/_modules/torch/nn/modules/distance.html
+++ b/main/_modules/torch/nn/modules/distance.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/dropout.html b/main/_modules/torch/nn/modules/dropout.html
index 31118b47765..b2020474bcc 100644
--- a/main/_modules/torch/nn/modules/dropout.html
+++ b/main/_modules/torch/nn/modules/dropout.html
@@ -299,7 +299,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/flatten.html b/main/_modules/torch/nn/modules/flatten.html
index 4f387876562..4b8566c0b92 100644
--- a/main/_modules/torch/nn/modules/flatten.html
+++ b/main/_modules/torch/nn/modules/flatten.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/fold.html b/main/_modules/torch/nn/modules/fold.html
index 7919465bab6..53bffae6663 100644
--- a/main/_modules/torch/nn/modules/fold.html
+++ b/main/_modules/torch/nn/modules/fold.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/instancenorm.html b/main/_modules/torch/nn/modules/instancenorm.html
index caf9252e1fe..b976d8c520e 100644
--- a/main/_modules/torch/nn/modules/instancenorm.html
+++ b/main/_modules/torch/nn/modules/instancenorm.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/lazy.html b/main/_modules/torch/nn/modules/lazy.html
index 90191f30156..3d9a540cddd 100644
--- a/main/_modules/torch/nn/modules/lazy.html
+++ b/main/_modules/torch/nn/modules/lazy.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/linear.html b/main/_modules/torch/nn/modules/linear.html
index c9c413e6c13..f60d3ddf874 100644
--- a/main/_modules/torch/nn/modules/linear.html
+++ b/main/_modules/torch/nn/modules/linear.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/loss.html b/main/_modules/torch/nn/modules/loss.html
index 697763045ea..b1d84611fec 100644
--- a/main/_modules/torch/nn/modules/loss.html
+++ b/main/_modules/torch/nn/modules/loss.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/module.html b/main/_modules/torch/nn/modules/module.html
index 420557d081e..11e29540b25 100644
--- a/main/_modules/torch/nn/modules/module.html
+++ b/main/_modules/torch/nn/modules/module.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/normalization.html b/main/_modules/torch/nn/modules/normalization.html
index da895147143..1ce090c17f8 100644
--- a/main/_modules/torch/nn/modules/normalization.html
+++ b/main/_modules/torch/nn/modules/normalization.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/padding.html b/main/_modules/torch/nn/modules/padding.html
index a1301d240a5..b511acc6d50 100644
--- a/main/_modules/torch/nn/modules/padding.html
+++ b/main/_modules/torch/nn/modules/padding.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/pixelshuffle.html b/main/_modules/torch/nn/modules/pixelshuffle.html
index 01a0c1b4cba..fa8ec5bc84e 100644
--- a/main/_modules/torch/nn/modules/pixelshuffle.html
+++ b/main/_modules/torch/nn/modules/pixelshuffle.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/pooling.html b/main/_modules/torch/nn/modules/pooling.html
index 151389138bb..3fb2d809d11 100644
--- a/main/_modules/torch/nn/modules/pooling.html
+++ b/main/_modules/torch/nn/modules/pooling.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/rnn.html b/main/_modules/torch/nn/modules/rnn.html
index 1304fbd6392..18efbc1cc16 100644
--- a/main/_modules/torch/nn/modules/rnn.html
+++ b/main/_modules/torch/nn/modules/rnn.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/sparse.html b/main/_modules/torch/nn/modules/sparse.html
index f61ca3e7114..80504a093ba 100644
--- a/main/_modules/torch/nn/modules/sparse.html
+++ b/main/_modules/torch/nn/modules/sparse.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/transformer.html b/main/_modules/torch/nn/modules/transformer.html
index fca66d3665f..4bca0c8a7ea 100644
--- a/main/_modules/torch/nn/modules/transformer.html
+++ b/main/_modules/torch/nn/modules/transformer.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/modules/upsampling.html b/main/_modules/torch/nn/modules/upsampling.html
index 4358e537191..005a73e4532 100644
--- a/main/_modules/torch/nn/modules/upsampling.html
+++ b/main/_modules/torch/nn/modules/upsampling.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/parallel/comm.html b/main/_modules/torch/nn/parallel/comm.html
index 17da6012805..164ba1bf14b 100644
--- a/main/_modules/torch/nn/parallel/comm.html
+++ b/main/_modules/torch/nn/parallel/comm.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/parallel/data_parallel.html b/main/_modules/torch/nn/parallel/data_parallel.html
index 35863a5dd5c..c62c1f75c9b 100644
--- a/main/_modules/torch/nn/parallel/data_parallel.html
+++ b/main/_modules/torch/nn/parallel/data_parallel.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/parallel/distributed.html b/main/_modules/torch/nn/parallel/distributed.html
index 6801c2e63c0..e6dfc54a183 100644
--- a/main/_modules/torch/nn/parallel/distributed.html
+++ b/main/_modules/torch/nn/parallel/distributed.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/parameter.html b/main/_modules/torch/nn/parameter.html
index 20f8780bad3..bf4c51095cc 100644
--- a/main/_modules/torch/nn/parameter.html
+++ b/main/_modules/torch/nn/parameter.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/clip_grad.html b/main/_modules/torch/nn/utils/clip_grad.html
index 0220cf94acb..d092ef22062 100644
--- a/main/_modules/torch/nn/utils/clip_grad.html
+++ b/main/_modules/torch/nn/utils/clip_grad.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -609,7 +609,8 @@ <h1>Source code for torch.nn.utils.clip_grad</h1><div class="highlight"><pre>
 <span></span><span class="c1"># mypy: allow-untyped-decorators</span>
 <span class="c1"># mypy: allow-untyped-defs</span>
 <span class="kn">import</span> <span class="nn">functools</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">import</span> <span class="nn">typing</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
 <span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">deprecated</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
@@ -628,7 +629,10 @@ <h1>Source code for torch.nn.utils.clip_grad</h1><div class="highlight"><pre>
 <span class="p">]</span>
 
 
-<span class="n">_tensor_or_tensors</span> <span class="o">=</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span>
+<span class="n">_tensor_or_tensors</span> <span class="o">=</span> <span class="n">Union</span><span class="p">[</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">typing</span><span class="o">.</span><span class="n">Iterable</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>  <span class="c1"># noqa: UP006 - needed until XLA&#39;s patch is updated</span>
+<span class="p">]</span>
 
 
 <span class="k">def</span> <span class="nf">_no_grad</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
@@ -681,13 +685,13 @@ <h1>Source code for torch.nn.utils.clip_grad</h1><div class="highlight"><pre>
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
     <span class="n">first_device</span> <span class="o">=</span> <span class="n">tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">device</span>
-    <span class="n">grouped_tensors</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span>
-        <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">],</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]],</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span>
+    <span class="n">grouped_tensors</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span>
+        <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]],</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span>
     <span class="p">]</span> <span class="o">=</span> <span class="n">_group_tensors_by_device_and_dtype</span><span class="p">(</span>
         <span class="p">[</span><span class="n">tensors</span><span class="p">]</span>  <span class="c1"># type: ignore[list-item]</span>
     <span class="p">)</span>  <span class="c1"># type: ignore[assignment]</span>
 
-    <span class="n">norms</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">norms</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
     <span class="k">for</span> <span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">_</span><span class="p">),</span> <span class="p">([</span><span class="n">device_tensors</span><span class="p">],</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">grouped_tensors</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
         <span class="k">if</span> <span class="p">(</span><span class="n">foreach</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">_has_foreach_support</span><span class="p">(</span><span class="n">device_tensors</span><span class="p">,</span> <span class="n">device</span><span class="p">))</span> <span class="ow">or</span> <span class="p">(</span>
             <span class="n">foreach</span> <span class="ow">and</span> <span class="n">_device_has_foreach_support</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
@@ -754,8 +758,8 @@ <h1>Source code for torch.nn.utils.clip_grad</h1><div class="highlight"><pre>
     <span class="n">max_norm</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">max_norm</span><span class="p">)</span>
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grads</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
         <span class="k">return</span>
-    <span class="n">grouped_grads</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span>
-        <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">],</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]],</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span>
+    <span class="n">grouped_grads</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span>
+        <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]],</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span>
     <span class="p">]</span> <span class="o">=</span> <span class="n">_group_tensors_by_device_and_dtype</span><span class="p">(</span>
         <span class="p">[</span><span class="n">grads</span><span class="p">]</span>
     <span class="p">)</span>  <span class="c1"># type: ignore[assignment]</span>
@@ -877,10 +881,10 @@ <h1>Source code for torch.nn.utils.clip_grad</h1><div class="highlight"><pre>
     <span class="k">for</span> <span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">_</span><span class="p">),</span> <span class="p">([</span><span class="n">grads</span><span class="p">],</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">grouped_grads</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
         <span class="k">if</span> <span class="p">(</span>
             <span class="n">foreach</span> <span class="ow">is</span> <span class="kc">None</span>
-            <span class="ow">and</span> <span class="n">_has_foreach_support</span><span class="p">(</span><span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grads</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
+            <span class="ow">and</span> <span class="n">_has_foreach_support</span><span class="p">(</span><span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grads</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
         <span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">foreach</span> <span class="ow">and</span> <span class="n">_device_has_foreach_support</span><span class="p">(</span><span class="n">device</span><span class="p">)):</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_clamp_min_</span><span class="p">(</span><span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grads</span><span class="p">),</span> <span class="o">-</span><span class="n">clip_value</span><span class="p">)</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_clamp_max_</span><span class="p">(</span><span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grads</span><span class="p">),</span> <span class="n">clip_value</span><span class="p">)</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_clamp_min_</span><span class="p">(</span><span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grads</span><span class="p">),</span> <span class="o">-</span><span class="n">clip_value</span><span class="p">)</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_clamp_max_</span><span class="p">(</span><span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grads</span><span class="p">),</span> <span class="n">clip_value</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">foreach</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
                 <span class="sa">f</span><span class="s2">&quot;foreach=True was passed, but can&#39;t use the foreach API on </span><span class="si">{</span><span class="n">device</span><span class="o">.</span><span class="n">type</span><span class="si">}</span><span class="s2"> tensors&quot;</span>
diff --git a/main/_modules/torch/nn/utils/convert_parameters.html b/main/_modules/torch/nn/utils/convert_parameters.html
index dcb95c719b9..6849457c576 100644
--- a/main/_modules/torch/nn/utils/convert_parameters.html
+++ b/main/_modules/torch/nn/utils/convert_parameters.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/fusion.html b/main/_modules/torch/nn/utils/fusion.html
index 2ad4d291280..ef6b355feed 100644
--- a/main/_modules/torch/nn/utils/fusion.html
+++ b/main/_modules/torch/nn/utils/fusion.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/init.html b/main/_modules/torch/nn/utils/init.html
index 7bc2017cba5..91a8d80116d 100644
--- a/main/_modules/torch/nn/utils/init.html
+++ b/main/_modules/torch/nn/utils/init.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/memory_format.html b/main/_modules/torch/nn/utils/memory_format.html
index 7733dbab302..8d3d6e68b58 100644
--- a/main/_modules/torch/nn/utils/memory_format.html
+++ b/main/_modules/torch/nn/utils/memory_format.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/parametrizations.html b/main/_modules/torch/nn/utils/parametrizations.html
index 435874715ba..265c6d23c8e 100644
--- a/main/_modules/torch/nn/utils/parametrizations.html
+++ b/main/_modules/torch/nn/utils/parametrizations.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/parametrize.html b/main/_modules/torch/nn/utils/parametrize.html
index a9b25468741..227e336c28a 100644
--- a/main/_modules/torch/nn/utils/parametrize.html
+++ b/main/_modules/torch/nn/utils/parametrize.html
@@ -299,7 +299,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/prune.html b/main/_modules/torch/nn/utils/prune.html
index 32bad5eeb25..92da1a0bdb7 100644
--- a/main/_modules/torch/nn/utils/prune.html
+++ b/main/_modules/torch/nn/utils/prune.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/rnn.html b/main/_modules/torch/nn/utils/rnn.html
index bc373f01f71..849c6d9398f 100644
--- a/main/_modules/torch/nn/utils/rnn.html
+++ b/main/_modules/torch/nn/utils/rnn.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/spectral_norm.html b/main/_modules/torch/nn/utils/spectral_norm.html
index 1c50fa1ca6e..05b73e81620 100644
--- a/main/_modules/torch/nn/utils/spectral_norm.html
+++ b/main/_modules/torch/nn/utils/spectral_norm.html
@@ -299,7 +299,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/stateless.html b/main/_modules/torch/nn/utils/stateless.html
index 6ad3cdc99a7..a383be63b43 100644
--- a/main/_modules/torch/nn/utils/stateless.html
+++ b/main/_modules/torch/nn/utils/stateless.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/nn/utils/weight_norm.html b/main/_modules/torch/nn/utils/weight_norm.html
index 1b4758a9879..3df973f3db0 100644
--- a/main/_modules/torch/nn/utils/weight_norm.html
+++ b/main/_modules/torch/nn/utils/weight_norm.html
@@ -299,7 +299,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/_modules/torch/onnx.html b/main/_modules/torch/onnx.html
index e8cb7475038..5acea872190 100644
--- a/main/_modules/torch/onnx.html
+++ b/main/_modules/torch/onnx.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_modules/torch/onnx/_internal/_exporter_legacy.html b/main/_modules/torch/onnx/_internal/_exporter_legacy.html
index 3b1f20aabe2..65ab0da7807 100644
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@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/onnx/_internal/exporter/_onnx_program.html b/main/_modules/torch/onnx/_internal/exporter/_onnx_program.html
index af550cb8487..886679bd9d3 100644
--- a/main/_modules/torch/onnx/_internal/exporter/_onnx_program.html
+++ b/main/_modules/torch/onnx/_internal/exporter/_onnx_program.html
@@ -299,7 +299,7 @@
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_modules/torch/onnx/_type_utils.html b/main/_modules/torch/onnx/_type_utils.html
index 5d48ff406a7..f28dfffb4d5 100644
--- a/main/_modules/torch/onnx/_type_utils.html
+++ b/main/_modules/torch/onnx/_type_utils.html
@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/_modules/torch/onnx/utils.html b/main/_modules/torch/onnx/utils.html
index 82cee3cb9f7..f8ae293bbad 100644
--- a/main/_modules/torch/onnx/utils.html
+++ b/main/_modules/torch/onnx/utils.html
@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/onnx/verification.html b/main/_modules/torch/onnx/verification.html
index 29173b91af4..bb290db4007 100644
--- a/main/_modules/torch/onnx/verification.html
+++ b/main/_modules/torch/onnx/verification.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim.html b/main/_modules/torch/optim.html
index eefd91f0cc4..1a37b57d687 100644
--- a/main/_modules/torch/optim.html
+++ b/main/_modules/torch/optim.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/_adafactor.html b/main/_modules/torch/optim/_adafactor.html
index 59c59122fa4..4bb739ef0b3 100644
--- a/main/_modules/torch/optim/_adafactor.html
+++ b/main/_modules/torch/optim/_adafactor.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/adadelta.html b/main/_modules/torch/optim/adadelta.html
index d0b73dc1cf3..2adb1f72b22 100644
--- a/main/_modules/torch/optim/adadelta.html
+++ b/main/_modules/torch/optim/adadelta.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/adagrad.html b/main/_modules/torch/optim/adagrad.html
index 8daf773c41a..ce99e4a7d97 100644
--- a/main/_modules/torch/optim/adagrad.html
+++ b/main/_modules/torch/optim/adagrad.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/adam.html b/main/_modules/torch/optim/adam.html
index cc79c25f33a..c2fde2c7caf 100644
--- a/main/_modules/torch/optim/adam.html
+++ b/main/_modules/torch/optim/adam.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/adamax.html b/main/_modules/torch/optim/adamax.html
index c71f63cfd10..5a793d9ad16 100644
--- a/main/_modules/torch/optim/adamax.html
+++ b/main/_modules/torch/optim/adamax.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -609,7 +609,7 @@
               
   <h1>Source code for torch.optim.adamax</h1><div class="highlight"><pre>
 <span></span><span class="c1"># mypy: allow-untyped-defs</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
@@ -640,7 +640,7 @@ <h1>Source code for torch.optim.adamax</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ParamsT</span><span class="p">,</span>
         <span class="n">lr</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2e-3</span><span class="p">,</span>
-        <span class="n">betas</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">),</span>
+        <span class="n">betas</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">),</span>
         <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-8</span><span class="p">,</span>
         <span class="n">weight_decay</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
         <span class="n">foreach</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
@@ -744,11 +744,11 @@ <h1>Source code for torch.optim.adamax</h1><div class="highlight"><pre>
                 <span class="n">loss</span> <span class="o">=</span> <span class="n">closure</span><span class="p">()</span>
 
         <span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
-            <span class="n">params_with_grad</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">exp_infs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">params_with_grad</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">exp_infs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
 
             <span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s2">&quot;betas&quot;</span><span class="p">]</span>
             <span class="n">eps</span> <span class="o">=</span> <span class="n">group</span><span class="p">[</span><span class="s2">&quot;eps&quot;</span><span class="p">]</span>
@@ -833,11 +833,11 @@ <h1>Source code for torch.optim.adamax</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">_single_tensor_adamax</span><span class="p">(</span>
-    <span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_infs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_infs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
     <span class="o">*</span><span class="p">,</span>
     <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
     <span class="n">beta1</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
@@ -907,11 +907,11 @@ <h1>Source code for torch.optim.adamax</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">_multi_tensor_adamax</span><span class="p">(</span>
-    <span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_infs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_infs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
     <span class="o">*</span><span class="p">,</span>
     <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
     <span class="n">beta1</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
@@ -949,11 +949,11 @@ <h1>Source code for torch.optim.adamax</h1><div class="highlight"><pre>
         <span class="n">grouped_exp_infs_</span><span class="p">,</span>
         <span class="n">grouped_state_steps_</span><span class="p">,</span>
     <span class="p">),</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">grouped_tensors</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-        <span class="n">grouped_params</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_params_</span><span class="p">)</span>
-        <span class="n">grouped_grads</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_grads_</span><span class="p">)</span>
-        <span class="n">grouped_exp_avgs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avgs_</span><span class="p">)</span>
-        <span class="n">grouped_exp_infs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_infs_</span><span class="p">)</span>
-        <span class="n">grouped_state_steps</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_state_steps_</span><span class="p">)</span>
+        <span class="n">grouped_params</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_params_</span><span class="p">)</span>
+        <span class="n">grouped_grads</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_grads_</span><span class="p">)</span>
+        <span class="n">grouped_exp_avgs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avgs_</span><span class="p">)</span>
+        <span class="n">grouped_exp_infs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_infs_</span><span class="p">)</span>
+        <span class="n">grouped_state_steps</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_state_steps_</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="n">has_complex</span><span class="p">:</span>
             <span class="n">_view_as_real</span><span class="p">(</span>
@@ -999,7 +999,7 @@ <h1>Source code for torch.optim.adamax</h1><div class="highlight"><pre>
         <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_add_</span><span class="p">(</span><span class="n">grouped_grads</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
         <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_maximum_</span><span class="p">(</span><span class="n">grouped_exp_infs</span><span class="p">,</span> <span class="n">grouped_grads</span><span class="p">)</span>
 
-        <span class="n">bias_corrections</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
+        <span class="n">bias_corrections</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
         <span class="k">if</span> <span class="n">capturable</span><span class="p">:</span>
             <span class="n">bias_corrections</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_pow</span><span class="p">(</span><span class="n">beta1</span><span class="p">,</span> <span class="n">grouped_state_steps</span><span class="p">)</span>
             <span class="c1"># foreach_sub doesn&#39;t allow a scalar as the first arg</span>
@@ -1020,11 +1020,11 @@ <h1>Source code for torch.optim.adamax</h1><div class="highlight"><pre>
 
 <span class="nd">@_disable_dynamo_if_unsupported</span><span class="p">(</span><span class="n">single_tensor_fn</span><span class="o">=</span><span class="n">_single_tensor_adamax</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">adamax</span><span class="p">(</span>
-    <span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_infs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_infs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
     <span class="c1"># kwonly args with defaults are not supported by functions compiled with torchscript issue #70627</span>
     <span class="c1"># setting this as kwarg for now as functional API is compiled by torch/distributed/optim</span>
     <span class="n">foreach</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
diff --git a/main/_modules/torch/optim/adamw.html b/main/_modules/torch/optim/adamw.html
index 208051cbc34..31ca4945b18 100644
--- a/main/_modules/torch/optim/adamw.html
+++ b/main/_modules/torch/optim/adamw.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/asgd.html b/main/_modules/torch/optim/asgd.html
index abf338d5e85..66efa492917 100644
--- a/main/_modules/torch/optim/asgd.html
+++ b/main/_modules/torch/optim/asgd.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/lbfgs.html b/main/_modules/torch/optim/lbfgs.html
index eade85c28c3..c679a919445 100644
--- a/main/_modules/torch/optim/lbfgs.html
+++ b/main/_modules/torch/optim/lbfgs.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/lr_scheduler.html b/main/_modules/torch/optim/lr_scheduler.html
index bffcee09447..f035edffe19 100644
--- a/main/_modules/torch/optim/lr_scheduler.html
+++ b/main/_modules/torch/optim/lr_scheduler.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/nadam.html b/main/_modules/torch/optim/nadam.html
index a77fbcd0e3b..dc8b18882a6 100644
--- a/main/_modules/torch/optim/nadam.html
+++ b/main/_modules/torch/optim/nadam.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
   <h1>Source code for torch.optim.nadam</h1><div class="highlight"><pre>
 <span></span><span class="c1"># mypy: allow-untyped-defs</span>
 <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Implementation for the NAdam algorithm.&quot;&quot;&quot;</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
@@ -642,7 +642,7 @@ <h1>Source code for torch.optim.nadam</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ParamsT</span><span class="p">,</span>
         <span class="n">lr</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2e-3</span><span class="p">,</span>
-        <span class="n">betas</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">),</span>
+        <span class="n">betas</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">),</span>
         <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-8</span><span class="p">,</span>
         <span class="n">weight_decay</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
         <span class="n">momentum_decay</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">4e-3</span><span class="p">,</span>
@@ -777,13 +777,13 @@ <h1>Source code for torch.optim.nadam</h1><div class="highlight"><pre>
                 <span class="n">loss</span> <span class="o">=</span> <span class="n">closure</span><span class="p">()</span>
 
         <span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
-            <span class="n">params_with_grad</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">mu_products</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span> <span class="n">group</span><span class="p">[</span><span class="s2">&quot;betas&quot;</span><span class="p">])</span>
+            <span class="n">params_with_grad</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">mu_products</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span> <span class="n">group</span><span class="p">[</span><span class="s2">&quot;betas&quot;</span><span class="p">])</span>
 
             <span class="n">has_complex</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_group</span><span class="p">(</span>
                 <span class="n">group</span><span class="p">,</span>
@@ -887,12 +887,12 @@ <h1>Source code for torch.optim.nadam</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">_single_tensor_nadam</span><span class="p">(</span>
-    <span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">mu_products</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">mu_products</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
     <span class="o">*</span><span class="p">,</span>
     <span class="n">beta1</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
     <span class="n">beta2</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
@@ -981,12 +981,12 @@ <h1>Source code for torch.optim.nadam</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">_multi_tensor_nadam</span><span class="p">(</span>
-    <span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">mu_products</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">mu_products</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
     <span class="o">*</span><span class="p">,</span>
     <span class="n">beta1</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
     <span class="n">beta2</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
@@ -1027,12 +1027,12 @@ <h1>Source code for torch.optim.nadam</h1><div class="highlight"><pre>
         <span class="n">grouped_mu_products_</span><span class="p">,</span>
         <span class="n">grouped_state_steps_</span><span class="p">,</span>
     <span class="p">),</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">grouped_tensors</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-        <span class="n">grouped_params</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_params_</span><span class="p">)</span>
-        <span class="n">grouped_grads</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_grads_</span><span class="p">)</span>
-        <span class="n">grouped_exp_avgs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avgs_</span><span class="p">)</span>
-        <span class="n">grouped_exp_avg_sqs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avg_sqs_</span><span class="p">)</span>
-        <span class="n">grouped_mu_products</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_mu_products_</span><span class="p">)</span>
-        <span class="n">grouped_state_steps</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_state_steps_</span><span class="p">)</span>
+        <span class="n">grouped_params</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_params_</span><span class="p">)</span>
+        <span class="n">grouped_grads</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_grads_</span><span class="p">)</span>
+        <span class="n">grouped_exp_avgs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avgs_</span><span class="p">)</span>
+        <span class="n">grouped_exp_avg_sqs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avg_sqs_</span><span class="p">)</span>
+        <span class="n">grouped_mu_products</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_mu_products_</span><span class="p">)</span>
+        <span class="n">grouped_state_steps</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_state_steps_</span><span class="p">)</span>
 
         <span class="c1"># handle complex</span>
         <span class="k">if</span> <span class="n">has_complex</span><span class="p">:</span>
@@ -1079,9 +1079,9 @@ <h1>Source code for torch.optim.nadam</h1><div class="highlight"><pre>
 
         <span class="n">exp_avg_sq_sqrt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_sqrt</span><span class="p">(</span><span class="n">grouped_exp_avg_sqs</span><span class="p">)</span>
 
-        <span class="n">bias_correction_sqrt</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
-        <span class="n">mus</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
-        <span class="n">mu_nexts</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
+        <span class="n">bias_correction_sqrt</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
+        <span class="n">mus</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
+        <span class="n">mu_nexts</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
         <span class="k">if</span> <span class="n">capturable</span><span class="p">:</span>
             <span class="c1"># mus will be beta1 * (1 - 0.5 * 0.96 ** (step * momentum_decay))</span>
             <span class="n">exponent</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_mul</span><span class="p">(</span><span class="n">grouped_state_steps</span><span class="p">,</span> <span class="n">momentum_decay</span><span class="p">)</span>
@@ -1189,12 +1189,12 @@ <h1>Source code for torch.optim.nadam</h1><div class="highlight"><pre>
 
 <span class="nd">@_disable_dynamo_if_unsupported</span><span class="p">(</span><span class="n">single_tensor_fn</span><span class="o">=</span><span class="n">_single_tensor_nadam</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">nadam</span><span class="p">(</span>
-    <span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">mu_products</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">mu_products</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
     <span class="c1"># kwonly args with defaults are not supported by functions compiled with torchscript issue #70627</span>
     <span class="c1"># setting this as kwarg for now as functional API is compiled by torch/distributed/optim</span>
     <span class="n">decoupled_weight_decay</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
diff --git a/main/_modules/torch/optim/optimizer.html b/main/_modules/torch/optim/optimizer.html
index 4d9aea8f1e5..1cdb90073f1 100644
--- a/main/_modules/torch/optim/optimizer.html
+++ b/main/_modules/torch/optim/optimizer.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/optim/radam.html b/main/_modules/torch/optim/radam.html
index 185c73d455b..48de28c6c0f 100644
--- a/main/_modules/torch/optim/radam.html
+++ b/main/_modules/torch/optim/radam.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
   <h1>Source code for torch.optim.radam</h1><div class="highlight"><pre>
 <span></span><span class="c1"># mypy: allow-untyped-defs</span>
 <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Implementation for the RAdam algorithm.&quot;&quot;&quot;</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">cast</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
@@ -641,7 +641,7 @@ <h1>Source code for torch.optim.radam</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">params</span><span class="p">:</span> <span class="n">ParamsT</span><span class="p">,</span>
         <span class="n">lr</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1e-3</span><span class="p">,</span>
-        <span class="n">betas</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">),</span>
+        <span class="n">betas</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">),</span>
         <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-8</span><span class="p">,</span>
         <span class="n">weight_decay</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
         <span class="n">decoupled_weight_decay</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
@@ -748,12 +748,12 @@ <h1>Source code for torch.optim.radam</h1><div class="highlight"><pre>
                 <span class="n">loss</span> <span class="o">=</span> <span class="n">closure</span><span class="p">()</span>
 
         <span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
-            <span class="n">params_with_grad</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span> <span class="n">group</span><span class="p">[</span><span class="s2">&quot;betas&quot;</span><span class="p">])</span>
+            <span class="n">params_with_grad</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">beta1</span><span class="p">,</span> <span class="n">beta2</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span> <span class="n">group</span><span class="p">[</span><span class="s2">&quot;betas&quot;</span><span class="p">])</span>
 
             <span class="n">has_complex</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_group</span><span class="p">(</span>
                 <span class="n">group</span><span class="p">,</span> <span class="n">params_with_grad</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">exp_avgs</span><span class="p">,</span> <span class="n">exp_avg_sqs</span><span class="p">,</span> <span class="n">state_steps</span>
@@ -862,11 +862,11 @@ <h1>Source code for torch.optim.radam</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">_single_tensor_radam</span><span class="p">(</span>
-    <span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
     <span class="o">*</span><span class="p">,</span>
     <span class="n">beta1</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
     <span class="n">beta2</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
@@ -961,11 +961,11 @@ <h1>Source code for torch.optim.radam</h1><div class="highlight"><pre>
 
 
 <span class="k">def</span> <span class="nf">_multi_tensor_radam</span><span class="p">(</span>
-    <span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
     <span class="o">*</span><span class="p">,</span>
     <span class="n">beta1</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
     <span class="n">beta2</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
@@ -1004,11 +1004,11 @@ <h1>Source code for torch.optim.radam</h1><div class="highlight"><pre>
         <span class="n">grouped_exp_avg_sqs_</span><span class="p">,</span>
         <span class="n">grouped_state_steps_</span><span class="p">,</span>
     <span class="p">),</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">grouped_tensors</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-        <span class="n">grouped_params</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_params_</span><span class="p">)</span>
-        <span class="n">grouped_grads</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_grads_</span><span class="p">)</span>
-        <span class="n">grouped_exp_avgs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avgs_</span><span class="p">)</span>
-        <span class="n">grouped_exp_avg_sqs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avg_sqs_</span><span class="p">)</span>
-        <span class="n">grouped_state_steps</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_state_steps_</span><span class="p">)</span>
+        <span class="n">grouped_params</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_params_</span><span class="p">)</span>
+        <span class="n">grouped_grads</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_grads_</span><span class="p">)</span>
+        <span class="n">grouped_exp_avgs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avgs_</span><span class="p">)</span>
+        <span class="n">grouped_exp_avg_sqs</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_exp_avg_sqs_</span><span class="p">)</span>
+        <span class="n">grouped_state_steps</span> <span class="o">=</span> <span class="n">cast</span><span class="p">(</span><span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">grouped_state_steps_</span><span class="p">)</span>
 
         <span class="c1"># Update steps</span>
         <span class="c1"># If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over</span>
@@ -1032,9 +1032,9 @@ <h1>Source code for torch.optim.radam</h1><div class="highlight"><pre>
         <span class="c1"># maximum length of the approximated SMA</span>
         <span class="n">rho_inf</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">beta2</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
         <span class="c1"># compute the length of the approximated SMA</span>
-        <span class="n">bias_correction1</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
-        <span class="n">bias_correction2</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
-        <span class="n">rho_t_list</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
+        <span class="n">bias_correction1</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
+        <span class="n">bias_correction2</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
+        <span class="n">rho_t_list</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">]]</span>
         <span class="k">if</span> <span class="n">capturable</span><span class="p">:</span>
             <span class="n">bias_correction1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_pow</span><span class="p">(</span><span class="n">beta2</span><span class="p">,</span> <span class="n">grouped_state_steps</span><span class="p">)</span>
             <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_neg_</span><span class="p">(</span><span class="n">bias_correction1</span><span class="p">)</span>
@@ -1157,11 +1157,11 @@ <h1>Source code for torch.optim.radam</h1><div class="highlight"><pre>
 
 <span class="nd">@_disable_dynamo_if_unsupported</span><span class="p">(</span><span class="n">single_tensor_fn</span><span class="o">=</span><span class="n">_single_tensor_radam</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">radam</span><span class="p">(</span>
-    <span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avgs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
-    <span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">params</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">grads</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">exp_avg_sqs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">state_steps</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
     <span class="c1"># kwonly args with defaults are not supported by functions compiled with torchscript issue #70627</span>
     <span class="c1"># setting this as kwarg for now as functional API is compiled by torch/distributed/optim</span>
     <span class="n">decoupled_weight_decay</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
diff --git a/main/_modules/torch/optim/rmsprop.html b/main/_modules/torch/optim/rmsprop.html
index 132100c576d..06ec626b144 100644
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index 0cc91c05d58..2fdf38dcff5 100644
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@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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index ca694fb42d3..2a9339f4fe0 100644
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diff --git a/main/_modules/torch/quasirandom.html b/main/_modules/torch/quasirandom.html
index 3e561838e4f..b2ac2e5a5fe 100644
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diff --git a/main/_modules/torch/serialization.html b/main/_modules/torch/serialization.html
index 70e220b329f..f26c131fe20 100644
--- a/main/_modules/torch/serialization.html
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diff --git a/main/_modules/torch/signal/windows/windows.html b/main/_modules/torch/signal/windows/windows.html
index 018b140b853..b83a7b02900 100644
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diff --git a/main/_modules/torch/sparse.html b/main/_modules/torch/sparse.html
index 2f9efd0d5be..7c9c72ef4ef 100644
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diff --git a/main/_modules/torch/storage.html b/main/_modules/torch/storage.html
index 94298efd89d..90a7efd9a17 100644
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diff --git a/main/_modules/torch/testing/_comparison.html b/main/_modules/torch/testing/_comparison.html
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@@ -299,7 +299,7 @@
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@@ -299,7 +299,7 @@
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@@ -299,7 +299,7 @@
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@@ -299,7 +299,7 @@
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diff --git a/main/_modules/torch/xpu.html b/main/_modules/torch/xpu.html
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@@ -299,7 +299,7 @@
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@@ -299,7 +299,7 @@
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diff --git a/main/_modules/typing.html b/main/_modules/typing.html
index 05fcfcd8366..d00f489c7e9 100644
--- a/main/_modules/typing.html
+++ b/main/_modules/typing.html
@@ -299,7 +299,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/_sources/generated/exportdb/index.rst.txt b/main/_sources/generated/exportdb/index.rst.txt
index 33f05991f91..5f105d85dd8 100644
--- a/main/_sources/generated/exportdb/index.rst.txt
+++ b/main/_sources/generated/exportdb/index.rst.txt
@@ -19,8 +19,8 @@ support in export please create an issue in the pytorch/pytorch repo with a modu
     :caption: Tags
 
     torch.escape-hatch
-    torch.dynamic-shape
     torch.cond
+    torch.dynamic-shape
     python.closure
     torch.dynamic-value
     python.data-structure
@@ -212,7 +212,7 @@ cond_branch_class_method
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -288,7 +288,7 @@ cond_branch_nested_function
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -361,7 +361,7 @@ cond_branch_nonlocal_variables
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -458,7 +458,7 @@ cond_closed_over_variable
 
 .. note::
 
-    Tags: :doc:`python.closure <python.closure>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`python.closure <python.closure>`
 
     Support Level: SUPPORTED
 
@@ -527,7 +527,7 @@ cond_operands
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -612,7 +612,7 @@ cond_predicate
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -669,7 +669,7 @@ constrain_as_size_example
 
 .. note::
 
-    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
+    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
 
     Support Level: SUPPORTED
 
@@ -736,7 +736,7 @@ constrain_as_value_example
 
 .. note::
 
-    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
+    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
 
     Support Level: SUPPORTED
 
@@ -1057,7 +1057,7 @@ dynamic_shape_map
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.map <torch.map>`
+    Tags: :doc:`torch.map <torch.map>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -1285,7 +1285,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`python.data-structure <python.data-structure>`, :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
+    Tags: :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.data-structure <python.data-structure>`
 
     Support Level: SUPPORTED
 
@@ -2107,6 +2107,6 @@ Result:
 
 .. code-block::
 
-    Unsupported: torch.* op returned non-Tensor int call_function <function sym_min at 0x7f6a40c213a0>
+    Unsupported: torch.* op returned non-Tensor int call_function <function sym_min at 0x7ff65abf64c0>
 
 
diff --git a/main/_sources/generated/exportdb/python.assert.rst.txt b/main/_sources/generated/exportdb/python.assert.rst.txt
index 7e3891cd83e..27f3bc55289 100644
--- a/main/_sources/generated/exportdb/python.assert.rst.txt
+++ b/main/_sources/generated/exportdb/python.assert.rst.txt
@@ -54,7 +54,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`python.data-structure <python.data-structure>`, :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
+    Tags: :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.data-structure <python.data-structure>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/python.closure.rst.txt b/main/_sources/generated/exportdb/python.closure.rst.txt
index 810d906087a..8529db02339 100644
--- a/main/_sources/generated/exportdb/python.closure.rst.txt
+++ b/main/_sources/generated/exportdb/python.closure.rst.txt
@@ -5,7 +5,7 @@ cond_closed_over_variable
 
 .. note::
 
-    Tags: :doc:`python.closure <python.closure>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`python.closure <python.closure>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/python.data-structure.rst.txt b/main/_sources/generated/exportdb/python.data-structure.rst.txt
index 76448cc4f4d..cb4f6023c0d 100644
--- a/main/_sources/generated/exportdb/python.data-structure.rst.txt
+++ b/main/_sources/generated/exportdb/python.data-structure.rst.txt
@@ -127,7 +127,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`python.data-structure <python.data-structure>`, :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
+    Tags: :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.data-structure <python.data-structure>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.cond.rst.txt b/main/_sources/generated/exportdb/torch.cond.rst.txt
index bc3d082795e..f1e960be726 100644
--- a/main/_sources/generated/exportdb/torch.cond.rst.txt
+++ b/main/_sources/generated/exportdb/torch.cond.rst.txt
@@ -5,7 +5,7 @@ cond_branch_class_method
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -81,7 +81,7 @@ cond_branch_nested_function
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -154,7 +154,7 @@ cond_branch_nonlocal_variables
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -251,7 +251,7 @@ cond_closed_over_variable
 
 .. note::
 
-    Tags: :doc:`python.closure <python.closure>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`python.closure <python.closure>`
 
     Support Level: SUPPORTED
 
@@ -320,7 +320,7 @@ cond_operands
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -405,7 +405,7 @@ cond_predicate
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt b/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt
index 12172395976..4994d68d0fb 100644
--- a/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt
+++ b/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt
@@ -5,7 +5,7 @@ cond_branch_class_method
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -81,7 +81,7 @@ cond_branch_nested_function
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -154,7 +154,7 @@ cond_branch_nonlocal_variables
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -251,7 +251,7 @@ cond_operands
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -336,7 +336,7 @@ cond_predicate
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.cond <torch.cond>`
+    Tags: :doc:`torch.cond <torch.cond>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -491,7 +491,7 @@ dynamic_shape_map
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.map <torch.map>`
+    Tags: :doc:`torch.map <torch.map>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -693,7 +693,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`python.data-structure <python.data-structure>`, :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
+    Tags: :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.data-structure <python.data-structure>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.dynamic-value.rst.txt b/main/_sources/generated/exportdb/torch.dynamic-value.rst.txt
index 8c272f63d95..1d3015e10a6 100644
--- a/main/_sources/generated/exportdb/torch.dynamic-value.rst.txt
+++ b/main/_sources/generated/exportdb/torch.dynamic-value.rst.txt
@@ -5,7 +5,7 @@ constrain_as_size_example
 
 .. note::
 
-    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
+    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
 
     Support Level: SUPPORTED
 
@@ -72,7 +72,7 @@ constrain_as_value_example
 
 .. note::
 
-    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
+    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.escape-hatch.rst.txt b/main/_sources/generated/exportdb/torch.escape-hatch.rst.txt
index 8b9041104f4..00e2ac7d3f1 100644
--- a/main/_sources/generated/exportdb/torch.escape-hatch.rst.txt
+++ b/main/_sources/generated/exportdb/torch.escape-hatch.rst.txt
@@ -57,7 +57,7 @@ constrain_as_size_example
 
 .. note::
 
-    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
+    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
 
     Support Level: SUPPORTED
 
@@ -124,7 +124,7 @@ constrain_as_value_example
 
 .. note::
 
-    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
+    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.map.rst.txt b/main/_sources/generated/exportdb/torch.map.rst.txt
index 67e1b453b7d..08c77566bae 100644
--- a/main/_sources/generated/exportdb/torch.map.rst.txt
+++ b/main/_sources/generated/exportdb/torch.map.rst.txt
@@ -5,7 +5,7 @@ dynamic_shape_map
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`torch.map <torch.map>`
+    Tags: :doc:`torch.map <torch.map>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.operator.rst.txt b/main/_sources/generated/exportdb/torch.operator.rst.txt
index 332388001cd..672d27e8b56 100644
--- a/main/_sources/generated/exportdb/torch.operator.rst.txt
+++ b/main/_sources/generated/exportdb/torch.operator.rst.txt
@@ -39,4 +39,4 @@ Result:
 
 .. code-block::
 
-    Unsupported: torch.* op returned non-Tensor int call_function <function sym_min at 0x7f6a40c213a0>
+    Unsupported: torch.* op returned non-Tensor int call_function <function sym_min at 0x7ff65abf64c0>
diff --git a/main/accelerator.html b/main/accelerator.html
index 7a374717a2b..404afde4288 100644
--- a/main/accelerator.html
+++ b/main/accelerator.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/amp.html b/main/amp.html
index 3effbf8a379..203fe24074d 100644
--- a/main/amp.html
+++ b/main/amp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -662,7 +662,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 <span id="id1"></span><h2><a class="toc-backref" href="#id4">Autocasting</a><a class="headerlink" href="#autocasting" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.amp.autocast_mode.is_autocast_available">
-<span class="sig-prename descclassname"><span class="pre">torch.amp.autocast_mode.</span></span><span class="sig-name descname"><span class="pre">is_autocast_available</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#is_autocast_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/amp/autocast_mode.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.autocast_mode.is_autocast_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.amp.autocast_mode.</span></span><span class="sig-name descname"><span class="pre">is_autocast_available</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#is_autocast_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/amp/autocast_mode.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.autocast_mode.is_autocast_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if autocast is available on <code class="xref py py-attr docutils literal notranslate"><span class="pre">device_type</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -678,7 +678,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autocast">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/amp/autocast_mode.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autocast" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/amp/autocast_mode.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autocast" title="Permalink to this definition">¶</a></dt>
 <dd><p>Instances of <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">autocast</span></code></a> serve as context managers or decorators that
 allow regions of your script to run in mixed precision.</p>
 <p>In these regions, ops run in an op-specific dtype chosen by autocast
@@ -844,7 +844,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.amp.custom_fwd">
-<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/amp/autocast_mode.py#L470"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/amp/autocast_mode.py#L470"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a helper decorator for <code class="docutils literal notranslate"><span class="pre">forward</span></code> methods of custom autograd functions.</p>
 <p>Autograd functions are subclasses of <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a>.
 See the <a class="reference internal" href="notes/amp_examples.html#amp-custom-examples"><span class="std std-ref">example page</span></a> for more detail.</p>
@@ -871,7 +871,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.amp.custom_bwd">
-<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/amp/autocast_mode.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/amp/autocast_mode.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a helper decorator for backward methods of custom autograd functions.</p>
 <p>Autograd functions are subclasses of <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a>.
 Ensures that <code class="docutils literal notranslate"><span class="pre">backward</span></code> executes with the same autocast state as <code class="docutils literal notranslate"><span class="pre">forward</span></code>.
@@ -887,7 +887,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.amp.autocast">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/amp/autocast_mode.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.autocast" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/amp/autocast_mode.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.autocast" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.autocast(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.autocast(&quot;cuda&quot;,</span> <span class="pre">args...)</span></code> instead.</p>
 <dl class="field-list simple">
@@ -896,21 +896,21 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.amp.custom_fwd">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/amp/autocast_mode.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/amp/autocast_mode.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.custom_fwd(args...)</span></code> is deprecated. Please use
 <code class="docutils literal notranslate"><span class="pre">torch.amp.custom_fwd(args...,</span> <span class="pre">device_type='cuda')</span></code> instead.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.amp.custom_bwd">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/amp/autocast_mode.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/amp/autocast_mode.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.custom_bwd(args...)</span></code> is deprecated. Please use
 <code class="docutils literal notranslate"><span class="pre">torch.amp.custom_bwd(args...,</span> <span class="pre">device_type='cuda')</span></code> instead.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cpu.amp.autocast">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/amp/autocast_mode.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.autocast" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/amp/autocast_mode.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.autocast" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a>.
 <code class="docutils literal notranslate"><span class="pre">torch.cpu.amp.autocast(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.autocast(&quot;cpu&quot;,</span> <span class="pre">args...)</span></code> instead.</p>
 <dl class="field-list simple">
@@ -941,7 +941,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.amp.GradScaler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/amp/grad_scaler.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/amp/grad_scaler.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.amp.GradScaler</span></code>.
 <code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.GradScaler(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.GradScaler(&quot;cuda&quot;,</span> <span class="pre">args...)</span></code> instead.</p>
 <dl class="field-list simple">
@@ -950,7 +950,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cpu.amp.GradScaler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/amp/grad_scaler.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/amp/grad_scaler.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.amp.GradScaler</span></code>.
 <code class="docutils literal notranslate"><span class="pre">torch.cpu.amp.GradScaler(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.GradScaler(&quot;cpu&quot;,</span> <span class="pre">args...)</span></code> instead.</p>
 <dl class="field-list simple">
diff --git a/main/autograd.html b/main/autograd.html
index 707e13e1d20..b5567538626 100644
--- a/main/autograd.html
+++ b/main/autograd.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -823,7 +823,7 @@ <h2>Tensor autograd functions<a class="headerlink" href="#tensor-autograd-functi
 <h2><span class="hidden-section">Function</span><a class="headerlink" href="#function" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.Function">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">Function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">Function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class to create custom <cite>autograd.Function</cite>.</p>
 <p>To create a custom <cite>autograd.Function</cite>, subclass this class and implement
 the <a class="reference internal" href="generated/torch.autograd.Function.forward.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> and <a class="reference internal" href="generated/torch.autograd.backward.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">backward()</span></code></a> static methods. Then, to use your custom
@@ -941,7 +941,7 @@ <h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this head
 <a class="reference internal" href="#torch.autograd.profiler.emit_itt" title="torch.autograd.profiler.emit_itt"><code class="xref py py-class docutils literal notranslate"><span class="pre">emit_itt</span></code></a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.profile">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_kineto</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_kineto</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that manages autograd profiler state and holds a summary of results.</p>
 <p>Under the hood it just records events of functions being executed in C++ and
 exposes those events to Python. You can wrap any code into it and it will
@@ -1050,7 +1050,7 @@ <h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this head
 </table>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.emit_nvtx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">emit_nvtx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#emit_nvtx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.emit_nvtx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">emit_nvtx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#emit_nvtx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.emit_nvtx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that makes every autograd operation emit an NVTX range.</p>
 <p>It is useful when running the program under nvprof:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">nvprof</span> <span class="o">--</span><span class="n">profile</span><span class="o">-</span><span class="n">from</span><span class="o">-</span><span class="n">start</span> <span class="n">off</span> <span class="o">-</span><span class="n">o</span> <span class="n">trace_name</span><span class="o">.</span><span class="n">prof</span> <span class="o">--</span> <span class="o">&lt;</span><span class="n">regular</span> <span class="n">command</span> <span class="n">here</span><span class="o">&gt;</span>
@@ -1119,7 +1119,7 @@ <h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.emit_itt">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">emit_itt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#emit_itt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L819"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.emit_itt" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">emit_itt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#emit_itt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L819"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.emit_itt" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that makes every autograd operation emit an ITT range.</p>
 <p>It is useful when running the program under Intel(R) VTune Profiler:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">vtune</span> <span class="o">&lt;--</span><span class="n">vtune</span><span class="o">-</span><span class="n">flags</span><span class="o">&gt;</span> <span class="o">&lt;</span><span class="n">regular</span> <span class="n">command</span> <span class="n">here</span><span class="o">&gt;</span>
@@ -1165,7 +1165,7 @@ <h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this head
 <h2>Debugging and anomaly detection<a class="headerlink" href="#debugging-and-anomaly-detection" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.detect_anomaly">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">detect_anomaly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">check_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/anomaly_mode.html#detect_anomaly"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/anomaly_mode.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.detect_anomaly" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">detect_anomaly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">check_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/anomaly_mode.html#detect_anomaly"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/anomaly_mode.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.detect_anomaly" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that enable anomaly detection for the autograd engine.</p>
 <p>This does two things:</p>
 <ul class="simple">
@@ -1235,7 +1235,7 @@ <h2>Debugging and anomaly detection<a class="headerlink" href="#debugging-and-an
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.set_detect_anomaly">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">set_detect_anomaly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/anomaly_mode.html#set_detect_anomaly"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/anomaly_mode.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.set_detect_anomaly" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">set_detect_anomaly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/anomaly_mode.html#set_detect_anomaly"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/anomaly_mode.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.set_detect_anomaly" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that sets the anomaly detection for the autograd engine on or off.</p>
 <p><code class="docutils literal notranslate"><span class="pre">set_detect_anomaly</span></code> will enable or disable the autograd anomaly detection
 based on its argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">mode</span></code>.
@@ -1311,7 +1311,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 Also see <a class="reference internal" href="notes/autograd.html#saved-tensors-hooks-doc"><span class="std std-ref">Hooks for saved tensors</span></a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.saved_tensors_hooks">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">saved_tensors_hooks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pack_hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unpack_hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#saved_tensors_hooks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.saved_tensors_hooks" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">saved_tensors_hooks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pack_hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unpack_hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#saved_tensors_hooks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.saved_tensors_hooks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that sets a pair of pack / unpack hooks for saved tensors.</p>
 <p>Use this context-manager to define how intermediary results of an operation
 should be packed before saving, and unpacked on retrieval.</p>
@@ -1372,7 +1372,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.save_on_cpu">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">save_on_cpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#save_on_cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.save_on_cpu" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">save_on_cpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#save_on_cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.save_on_cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager under which tensors saved by the forward pass will be stored on cpu, then retrieved for backward.</p>
 <p>When performing operations within this context manager, intermediary
 results saved in the graph during the forward pass will be moved to CPU,
@@ -1412,7 +1412,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.disable_saved_tensors_hooks">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">disable_saved_tensors_hooks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">error_message</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#disable_saved_tensors_hooks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.disable_saved_tensors_hooks" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">disable_saved_tensors_hooks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">error_message</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#disable_saved_tensors_hooks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.disable_saved_tensors_hooks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that disables the saved tensors default hooks feature.</p>
 <p>Useful for if you are creating a feature that does not work with saved
 tensors default hooks.</p>
@@ -1438,7 +1438,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.register_multi_grad_hook">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">register_multi_grad_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#register_multi_grad_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.register_multi_grad_hook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">register_multi_grad_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#register_multi_grad_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.register_multi_grad_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a multi-grad backward hook.</p>
 <p>There are two supported modes: <code class="docutils literal notranslate"><span class="pre">&quot;all&quot;</span></code> and <code class="docutils literal notranslate"><span class="pre">&quot;any&quot;</span></code>.</p>
 <p>Under the <code class="docutils literal notranslate"><span class="pre">&quot;all&quot;</span></code> mode, the hook will be called after gradients with respect to every tensor in
@@ -1489,7 +1489,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.allow_mutation_on_saved_tensors">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">allow_mutation_on_saved_tensors</span></span><a class="reference internal" href="_modules/torch/autograd/graph.html#allow_mutation_on_saved_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L717"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.allow_mutation_on_saved_tensors" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">allow_mutation_on_saved_tensors</span></span><a class="reference internal" href="_modules/torch/autograd/graph.html#allow_mutation_on_saved_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L717"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.allow_mutation_on_saved_tensors" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager under which mutating tensors saved for backward is allowed.</p>
 <p>Under this context manager, tensors saved for backward are cloned on mutation,
 so the original version can still be used during backward. Normally, mutating a tensor
@@ -1525,7 +1525,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.GradientEdge">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">GradientEdge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_nr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#GradientEdge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.GradientEdge" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">GradientEdge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_nr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#GradientEdge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.GradientEdge" title="Permalink to this definition">¶</a></dt>
 <dd><p>Object representing a given gradient edge within the autograd graph.</p>
 <p>To get the gradient edge where a given Tensor gradient will be computed,
 you can do <code class="docutils literal notranslate"><span class="pre">edge</span> <span class="pre">=</span> <span class="pre">autograd.graph.get_gradient_edge(tensor)</span></code>.</p>
@@ -1535,7 +1535,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.graph.get_gradient_edge">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">get_gradient_edge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#get_gradient_edge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.get_gradient_edge" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">get_gradient_edge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#get_gradient_edge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.get_gradient_edge" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the gradient edge for computing the gradient of the given Tensor.</p>
 <p>In particular, it is equivalent to call
 <code class="docutils literal notranslate"><span class="pre">g</span> <span class="pre">=</span> <span class="pre">autograd.grad(loss,</span> <span class="pre">input)</span></code> and <code class="docutils literal notranslate"><span class="pre">g</span> <span class="pre">=</span> <span class="pre">autograd.grad(loss,</span> <span class="pre">get_gradient_edge(input))</span></code>.</p>
diff --git a/main/backends.html b/main/backends.html
index 675b92884d6..e3f9fb846a9 100644
--- a/main/backends.html
+++ b/main/backends.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -628,7 +628,7 @@
 <span id="torch-backends-cpu"></span><h2>torch.backends.cpu<a class="headerlink" href="#module-torch.backends.cpu" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cpu.get_cpu_capability">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cpu.</span></span><span class="sig-name descname"><span class="pre">get_cpu_capability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cpu.html#get_cpu_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cpu/__init__.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cpu.get_cpu_capability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cpu.</span></span><span class="sig-name descname"><span class="pre">get_cpu_capability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cpu.html#get_cpu_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cpu/__init__.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cpu.get_cpu_capability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return cpu capability as a string value.</p>
 <p>Possible values:
 - “DEFAULT”
@@ -650,7 +650,7 @@
 <span id="torch-backends-cuda"></span><h2>torch.backends.cuda<a class="headerlink" href="#module-torch.backends.cuda" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.is_built">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.is_built" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.is_built" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with CUDA support.</p>
 <p>Note that this doesn’t necessarily mean CUDA is available; just that if this PyTorch
 binary were run on a machine with working CUDA drivers and devices, we would be able to use it.</p>
@@ -702,7 +702,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.preferred_blas_library">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_blas_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_blas_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_blas_library" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_blas_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_blas_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_blas_library" title="Permalink to this definition">¶</a></dt>
 <dd><p>Override the library PyTorch uses for BLAS operations. Choose between cuBLAS, cuBLASLt, and CK [ROCm-only].</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -734,7 +734,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.preferred_rocm_fa_library">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_rocm_fa_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_rocm_fa_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_rocm_fa_library" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_rocm_fa_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_rocm_fa_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_rocm_fa_library" title="Permalink to this definition">¶</a></dt>
 <dd><p>[ROCm-only]
 Override the backend PyTorch uses in ROCm environments for Flash Attention. Choose between AOTriton and CK</p>
 <div class="admonition warning">
@@ -764,7 +764,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.preferred_linalg_library">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_linalg_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_linalg_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_linalg_library" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_linalg_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_linalg_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_linalg_library" title="Permalink to this definition">¶</a></dt>
 <dd><p>Override the heuristic PyTorch uses to choose between cuSOLVER and MAGMA for CUDA linear algebra operations.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -819,7 +819,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.flash_sdp_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">flash_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#flash_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.flash_sdp_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">flash_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#flash_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.flash_sdp_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -829,7 +829,7 @@
 
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-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_mem_efficient_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_mem_efficient_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L351"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_mem_efficient_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_mem_efficient_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_mem_efficient_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L351"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_mem_efficient_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -841,7 +841,7 @@
 
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 <dt class="sig sig-object py" id="torch.backends.cuda.mem_efficient_sdp_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">mem_efficient_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#mem_efficient_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.mem_efficient_sdp_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">mem_efficient_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#mem_efficient_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.mem_efficient_sdp_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -851,7 +851,7 @@
 
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 <dt class="sig sig-object py" id="torch.backends.cuda.enable_flash_sdp">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_flash_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_flash_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_flash_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_flash_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_flash_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_flash_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -863,7 +863,7 @@
 
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 <dt class="sig sig-object py" id="torch.backends.cuda.math_sdp_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">math_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#math_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.math_sdp_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">math_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#math_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.math_sdp_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -873,7 +873,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.enable_math_sdp">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_math_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_math_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L369"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_math_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_math_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_math_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L369"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_math_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -885,7 +885,7 @@
 
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 <dt class="sig sig-object py" id="torch.backends.cuda.fp16_bf16_reduction_math_sdp_allowed">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">fp16_bf16_reduction_math_sdp_allowed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#fp16_bf16_reduction_math_sdp_allowed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.fp16_bf16_reduction_math_sdp_allowed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">fp16_bf16_reduction_math_sdp_allowed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#fp16_bf16_reduction_math_sdp_allowed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.fp16_bf16_reduction_math_sdp_allowed" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -895,7 +895,7 @@
 
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 <dt class="sig sig-object py" id="torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">allow_fp16_bf16_reduction_math_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#allow_fp16_bf16_reduction_math_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">allow_fp16_bf16_reduction_math_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#allow_fp16_bf16_reduction_math_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -907,7 +907,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.cudnn_sdp_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">cudnn_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#cudnn_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.cudnn_sdp_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">cudnn_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#cudnn_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.cudnn_sdp_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -917,7 +917,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.enable_cudnn_sdp">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_cudnn_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_cudnn_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_cudnn_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_cudnn_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_cudnn_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_cudnn_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -929,7 +929,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.is_flash_attention_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">is_flash_attention_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_flash_attention_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L396"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.is_flash_attention_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">is_flash_attention_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_flash_attention_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L396"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.is_flash_attention_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if PyTorch was built with FlashAttention for scaled_dot_product_attention.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -948,7 +948,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.can_use_flash_attention">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_flash_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_flash_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_flash_attention" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_flash_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_flash_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_flash_attention" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if FlashAttention can be utilized in scaled_dot_product_attention.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -976,7 +976,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.can_use_efficient_attention">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_efficient_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_efficient_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_efficient_attention" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_efficient_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_efficient_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_efficient_attention" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if efficient_attention can be utilized in scaled_dot_product_attention.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1004,7 +1004,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.can_use_cudnn_attention">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_cudnn_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_cudnn_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L449"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_cudnn_attention" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_cudnn_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_cudnn_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L449"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_cudnn_attention" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if cudnn_attention can be utilized in scaled_dot_product_attention.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1032,7 +1032,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.sdp_kernel">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">sdp_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_flash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_math</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_mem_efficient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_cudnn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#sdp_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cuda/__init__.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.sdp_kernel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">sdp_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_flash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_math</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_mem_efficient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_cudnn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#sdp_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.sdp_kernel" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -1048,13 +1048,13 @@
 <span id="torch-backends-cudnn"></span><h2>torch.backends.cudnn<a class="headerlink" href="#module-torch.backends.cudnn" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cudnn.version">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cudnn/__init__.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cudnn/__init__.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the version of cuDNN.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cudnn.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cudnn/__init__.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cudnn/__init__.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if CUDNN is currently available.</p>
 </dd></dl>
 
@@ -1100,7 +1100,7 @@
 <span id="torch-backends-cusparselt"></span><h2>torch.backends.cusparselt<a class="headerlink" href="#module-torch.backends.cusparselt" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cusparselt.version">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cusparselt.</span></span><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cusparselt.html#version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cusparselt/__init__.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cusparselt.version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cusparselt.</span></span><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cusparselt.html#version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cusparselt/__init__.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cusparselt.version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the version of cuSPARSELt</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1111,7 +1111,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cusparselt.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cusparselt.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cusparselt.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/cusparselt/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cusparselt.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cusparselt.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cusparselt.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cusparselt/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cusparselt.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if cuSPARSELt is currently available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1125,7 +1125,7 @@
 <span id="torch-backends-mha"></span><h2>torch.backends.mha<a class="headerlink" href="#module-torch.backends.mha" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mha.get_fastpath_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mha.</span></span><span class="sig-name descname"><span class="pre">get_fastpath_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mha.html#get_fastpath_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/mha/__init__.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mha.get_fastpath_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mha.</span></span><span class="sig-name descname"><span class="pre">get_fastpath_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mha.html#get_fastpath_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mha/__init__.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mha.get_fastpath_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether fast path for TransformerEncoder and MultiHeadAttention
 is enabled, or <code class="docutils literal notranslate"><span class="pre">True</span></code> if jit is scripting.</p>
 <div class="admonition note">
@@ -1142,7 +1142,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mha.set_fastpath_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mha.</span></span><span class="sig-name descname"><span class="pre">set_fastpath_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mha.html#set_fastpath_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/mha/__init__.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mha.set_fastpath_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mha.</span></span><span class="sig-name descname"><span class="pre">set_fastpath_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mha.html#set_fastpath_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mha/__init__.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mha.set_fastpath_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether fast path is enabled</p>
 <dl class="field-list simple">
 </dl>
@@ -1153,7 +1153,7 @@
 <span id="torch-backends-mps"></span><h2>torch.backends.mps<a class="headerlink" href="#module-torch.backends.mps" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mps.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/mps/__init__.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mps/__init__.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if MPS is currently available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1164,7 +1164,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mps.is_built">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/mps/__init__.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_built" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mps/__init__.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_built" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with MPS support.</p>
 <p>Note that this doesn’t necessarily mean MPS is available; just that
 if this PyTorch binary were run a machine with working MPS drivers
@@ -1181,13 +1181,13 @@
 <span id="torch-backends-mkl"></span><h2>torch.backends.mkl<a class="headerlink" href="#module-torch.backends.mkl" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mkl.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/mkl/__init__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mkl/__init__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with MKL support.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.backends.mkl.verbose">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/mkl/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.verbose" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mkl/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.verbose" title="Permalink to this definition">¶</a></dt>
 <dd><p>On-demand oneMKL verbosing functionality.</p>
 <p>To make it easier to debug performance issues, oneMKL can dump verbose
 messages containing execution information like duration while executing
@@ -1219,13 +1219,13 @@
 <span id="torch-backends-mkldnn"></span><h2>torch.backends.mkldnn<a class="headerlink" href="#module-torch.backends.mkldnn" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mkldnn.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/mkldnn/__init__.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mkldnn/__init__.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with MKL-DNN support.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.backends.mkldnn.verbose">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/mkldnn/__init__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.verbose" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mkldnn/__init__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.verbose" title="Permalink to this definition">¶</a></dt>
 <dd><p>On-demand oneDNN (former MKL-DNN) verbosing functionality.</p>
 <p>To make it easier to debug performance issues, oneDNN can dump verbose
 messages containing information like kernel size, input data size and
@@ -1258,19 +1258,19 @@
 <span id="torch-backends-nnpack"></span><h2>torch.backends.nnpack<a class="headerlink" href="#module-torch.backends.nnpack" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.nnpack.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/nnpack/__init__.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/nnpack/__init__.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with NNPACK support.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.nnpack.flags">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">flags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/nnpack/__init__.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.flags" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">flags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/nnpack/__init__.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.flags" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager for setting if nnpack is enabled globally</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.nnpack.set_flags">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">set_flags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#set_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/nnpack/__init__.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.set_flags" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">set_flags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#set_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/nnpack/__init__.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.set_flags" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set if nnpack is enabled globally</p>
 </dd></dl>
 
@@ -1279,7 +1279,7 @@
 <span id="torch-backends-openmp"></span><h2>torch.backends.openmp<a class="headerlink" href="#module-torch.backends.openmp" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.openmp.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.openmp.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/openmp.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/openmp/__init__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.openmp.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.openmp.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/openmp.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/openmp/__init__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.openmp.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with OpenMP support.</p>
 </dd></dl>
 
@@ -1288,7 +1288,7 @@
 <span id="torch-backends-opt-einsum"></span><h2>torch.backends.opt_einsum<a class="headerlink" href="#module-torch.backends.opt_einsum" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.opt_einsum.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/opt_einsum/__init__.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/opt_einsum/__init__.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if opt_einsum is currently available.</p>
 <p>You must install opt-einsum in order for torch to automatically optimize einsum. To
 make opt-einsum available, you can install it along with torch: <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torch[opt-einsum]</span></code>
@@ -1304,7 +1304,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.opt_einsum.get_opt_einsum">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">get_opt_einsum</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#get_opt_einsum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/backends/opt_einsum/__init__.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.get_opt_einsum" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">get_opt_einsum</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#get_opt_einsum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/opt_einsum/__init__.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.get_opt_einsum" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the opt_einsum package if opt_einsum is currently available, else None.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/benchmark_utils.html b/main/benchmark_utils.html
index c92fe3273db..608bfd2c745 100644
--- a/main/benchmark_utils.html
+++ b/main/benchmark_utils.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="benchmark-utils-torch-utils-benchmark"></span><h1>Benchmark Utils - torch.utils.benchmark<a class="headerlink" href="#module-torch.utils.benchmark" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Timer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stmt='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_setup=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timer=&lt;built-in</span> <span class="pre">function</span> <span class="pre">perf_counter&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">globals=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">language=Language.PYTHON</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/timer.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Timer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stmt='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_setup=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timer=&lt;built-in</span> <span class="pre">function</span> <span class="pre">perf_counter&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">globals=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">language=Language.PYTHON</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper class for measuring execution time of PyTorch statements.</p>
 <p>For a full tutorial on how to use this class, see:
 <a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/benchmark.html">https://pytorch.org/tutorials/recipes/recipes/benchmark.html</a></p>
@@ -714,7 +714,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer.adaptive_autorange">
-<span class="sig-name descname"><span class="pre">adaptive_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.adaptive_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/timer.py#L391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.adaptive_autorange" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">adaptive_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.adaptive_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.adaptive_autorange" title="Permalink to this definition">¶</a></dt>
 <dd><p>Similar to <cite>blocked_autorange</cite> but also checks for variablility in measurements
 and repeats until iqr/median is smaller than <cite>threshold</cite> or <cite>max_run_time</cite> is reached.</p>
 <p>At a high level, adaptive_autorange executes the following pseudo-code:</p>
@@ -755,7 +755,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer.blocked_autorange">
-<span class="sig-name descname"><span class="pre">blocked_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.blocked_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/timer.py#L331"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.blocked_autorange" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">blocked_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.blocked_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L331"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.blocked_autorange" title="Permalink to this definition">¶</a></dt>
 <dd><p>Measure many replicates while keeping timer overhead to a minimum.</p>
 <p>At a high level, blocked_autorange executes the following pseudo-code:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`setup`
@@ -800,7 +800,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer.collect_callgrind">
-<span class="sig-name descname"><span class="pre">collect_callgrind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">collect_baseline</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_out_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch.utils.benchmark.CallgrindStats" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats"><span class="pre">CallgrindStats</span></a></span></span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.collect_callgrind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/timer.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.collect_callgrind" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">collect_callgrind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">collect_baseline</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_out_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch.utils.benchmark.CallgrindStats" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats"><span class="pre">CallgrindStats</span></a></span></span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.collect_callgrind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.collect_callgrind" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-name descname"><span class="pre">collect_callgrind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">collect_baseline</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_out_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.utils.benchmark.CallgrindStats" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats"><span class="pre">torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
 <dd><p>Collect instruction counts using Callgrind.</p>
@@ -835,7 +835,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer.timeit">
-<span class="sig-name descname"><span class="pre">timeit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.timeit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/timer.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.timeit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">timeit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.timeit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.timeit" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mirrors the semantics of timeit.Timer.timeit().</p>
 <p>Execute the main statement (<cite>stmt</cite>) <cite>number</cite> times.
 <a class="reference external" href="https://docs.python.org/3/library/timeit.html#timeit.Timer.timeit">https://docs.python.org/3/library/timeit.html#timeit.Timer.timeit</a></p>
@@ -850,7 +850,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Measurement">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Measurement</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raw_times</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/common.html#Measurement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/common.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Measurement</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raw_times</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/common.html#Measurement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/common.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement" title="Permalink to this definition">¶</a></dt>
 <dd><p>The result of a Timer measurement.</p>
 <p>This class stores one or more measurements of a given statement. It is
 serializable and provides several convenience methods
@@ -859,7 +859,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Measurement.merge">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">merge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measurements</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/common.html#Measurement.merge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/common.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement.merge" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">merge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measurements</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/common.html#Measurement.merge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/common.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement.merge" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convenience method for merging replicates.</p>
 <p>Merge will extrapolate times to <cite>number_per_run=1</cite> and will not
 transfer any metadata. (Since it might differ between replicates)</p>
@@ -889,7 +889,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">CallgrindStats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">built_with_debug_symbols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_callgrind_out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">CallgrindStats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">built_with_debug_symbols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_callgrind_out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Top level container for Callgrind results collected by Timer.</p>
 <p>Manipulation is generally done using the FunctionCounts class, which is
 obtained by calling <cite>CallgrindStats.stats(…)</cite>. Several convenience
@@ -899,7 +899,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.as_standardized">
-<span class="sig-name descname"><span class="pre">as_standardized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.as_standardized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.as_standardized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">as_standardized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.as_standardized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.as_standardized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Strip library names and some prefixes from function strings.</p>
 <p>When comparing two different sets of instruction counts, on stumbling
 block can be path prefixes. Callgrind includes the full filepath
@@ -928,7 +928,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.counts">
-<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">denoise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.counts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">denoise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.counts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the total number of instructions executed.</p>
 <p>See <cite>FunctionCounts.denoise()</cite> for an explanation of the <cite>denoise</cite> arg.</p>
 <dl class="field-list simple">
@@ -940,7 +940,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.delta">
-<span class="sig-name descname"><span class="pre">delta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.delta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.delta" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">delta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.delta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.delta" title="Permalink to this definition">¶</a></dt>
 <dd><p>Diff two sets of counts.</p>
 <p>One common reason to collect instruction counts is to determine the
 the effect that a particular change will have on the number of instructions
@@ -958,7 +958,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.stats">
-<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns detailed function counts.</p>
 <p>Conceptually, the FunctionCounts returned can be thought of as a tuple
 of (count, path_and_function_name) tuples.</p>
@@ -978,7 +978,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">FunctionCounts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">FunctionCounts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Container for manipulating Callgrind results.</p>
 <dl class="simple">
 <dt>It supports:</dt><dd><ol class="arabic simple">
@@ -995,7 +995,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts.denoise">
-<span class="sig-name descname"><span class="pre">denoise</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.denoise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.denoise" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">denoise</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.denoise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.denoise" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove known noisy instructions.</p>
 <p>Several instructions in the CPython interpreter are rather noisy. These
 instructions involve unicode to dictionary lookups which Python uses to
@@ -1011,7 +1011,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts.filter">
-<span class="sig-name descname"><span class="pre">filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filter_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.filter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.filter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filter_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.filter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.filter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Keep only the elements where <cite>filter_fn</cite> applied to function name returns True.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1022,7 +1022,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">map_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.transform" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">map_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.transform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <cite>map_fn</cite> to all of the function names.</p>
 <p>This can be used to regularize function names (e.g. stripping irrelevant
 parts of the file path), coalesce entries by mapping multiple functions
@@ -1038,7 +1038,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Compare</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/compare.py#L269"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Compare</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L269"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper class for displaying the results of many measurements in a
 formatted table.</p>
 <p>The table format is based on the information fields provided in
@@ -1054,33 +1054,33 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.colorize">
-<span class="sig-name descname"><span class="pre">colorize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rowwise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.colorize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/compare.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.colorize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">colorize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rowwise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.colorize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.colorize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Colorize formatted table.</p>
 <p>Colorize columnwise by default.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.extend_results">
-<span class="sig-name descname"><span class="pre">extend_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.extend_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/compare.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.extend_results" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extend_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.extend_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.extend_results" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append results to already stored ones.</p>
 <p>All added results must be instances of <code class="docutils literal notranslate"><span class="pre">Measurement</span></code>.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.highlight_warnings">
-<span class="sig-name descname"><span class="pre">highlight_warnings</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.highlight_warnings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/compare.py#L318"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.highlight_warnings" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">highlight_warnings</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.highlight_warnings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L318"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.highlight_warnings" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enables warning highlighting when building formatted table.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.print">
-<span class="sig-name descname"><span class="pre">print</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.print"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/compare.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.print" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.print"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.print" title="Permalink to this definition">¶</a></dt>
 <dd><p>Print formatted table</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.trim_significant_figures">
-<span class="sig-name descname"><span class="pre">trim_significant_figures</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.trim_significant_figures"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/benchmark/utils/compare.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.trim_significant_figures" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">trim_significant_figures</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.trim_significant_figures"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.trim_significant_figures" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enables trimming of significant figures when building the formatted table.</p>
 </dd></dl>
 
diff --git a/main/bottleneck.html b/main/bottleneck.html
index 8e65bfdd922..6317eb4492d 100644
--- a/main/bottleneck.html
+++ b/main/bottleneck.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/checkpoint.html b/main/checkpoint.html
index f1783286e91..2befd75e762 100644
--- a/main/checkpoint.html
+++ b/main/checkpoint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -640,7 +640,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.checkpoint">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_fn=&lt;function</span> <span class="pre">noop_context_fn&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">determinism_check='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/checkpoint.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_fn=&lt;function</span> <span class="pre">noop_context_fn&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">determinism_check='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checkpoint a model or part of the model.</p>
 <p>Activation checkpointing is a technique that trades compute for memory.
 Instead of keeping tensors needed for backward alive until they are used in
@@ -752,7 +752,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.checkpoint_sequential">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint_sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">segments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint_sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/checkpoint.py#L503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint_sequential" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint_sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">segments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint_sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint_sequential" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checkpoint a sequential model to save memory.</p>
 <p>Sequential models execute a list of modules/functions in order
 (sequentially). Therefore, we can divide such a model in various segments
@@ -802,7 +802,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.set_checkpoint_debug_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">set_checkpoint_debug_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#set_checkpoint_debug_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/checkpoint.py#L43"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.set_checkpoint_debug_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">set_checkpoint_debug_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#set_checkpoint_debug_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L43"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.set_checkpoint_debug_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that sets whether checkpoint should print additional debug
 information when running. See the <code class="docutils literal notranslate"><span class="pre">debug</span></code> flag for
 <a class="reference internal" href="#torch.utils.checkpoint.checkpoint" title="torch.utils.checkpoint.checkpoint"><code class="xref py py-func docutils literal notranslate"><span class="pre">checkpoint()</span></code></a> for more information. Note that
@@ -818,7 +818,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.CheckpointPolicy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">CheckpointPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#CheckpointPolicy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/checkpoint.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.CheckpointPolicy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">CheckpointPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#CheckpointPolicy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.CheckpointPolicy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enum for specifying the policy for checkpointing during backpropagation.</p>
 <p>The following policies are supported:</p>
 <ul class="simple">
@@ -842,7 +842,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.SelectiveCheckpointContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SelectiveCheckpointContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_recompute</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#SelectiveCheckpointContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/checkpoint.py#L1199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.SelectiveCheckpointContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SelectiveCheckpointContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_recompute</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#SelectiveCheckpointContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L1199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.SelectiveCheckpointContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context passed to policy function during selective checkpointing.</p>
 <p>This class is used to pass relevant metadata to the policy function during
 selective checkpointing. The metadata includes whether the current invocation
@@ -865,7 +865,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.create_selective_checkpoint_contexts">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">create_selective_checkpoint_contexts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">policy_fn_or_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_cache_entry_mutation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#create_selective_checkpoint_contexts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/checkpoint.py#L1333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.create_selective_checkpoint_contexts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">create_selective_checkpoint_contexts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">policy_fn_or_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_cache_entry_mutation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#create_selective_checkpoint_contexts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L1333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.create_selective_checkpoint_contexts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper to avoid recomputing certain ops during activation checkpointing.</p>
 <p>Use this with <cite>torch.utils.checkpoint.checkpoint</cite> to control which
 operations are recomputed during the backward pass.</p>
diff --git a/main/community/build_ci_governance.html b/main/community/build_ci_governance.html
index e2d36501238..2aaff1f63f3 100644
--- a/main/community/build_ci_governance.html
+++ b/main/community/build_ci_governance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/community/contribution_guide.html b/main/community/contribution_guide.html
index eda0c269a38..46e81fb47c6 100644
--- a/main/community/contribution_guide.html
+++ b/main/community/contribution_guide.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/community/design.html b/main/community/design.html
index 8f4c75096b8..80f8e80a975 100644
--- a/main/community/design.html
+++ b/main/community/design.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/community/governance.html b/main/community/governance.html
index fb8c1814fb8..0e842728e3a 100644
--- a/main/community/governance.html
+++ b/main/community/governance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/community/persons_of_interest.html b/main/community/persons_of_interest.html
index a401dd61adc..9eaa9144a01 100644
--- a/main/community/persons_of_interest.html
+++ b/main/community/persons_of_interest.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/complex_numbers.html b/main/complex_numbers.html
index 56cd399a23b..c64d80ba49a 100644
--- a/main/complex_numbers.html
+++ b/main/complex_numbers.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/cond.html b/main/cond.html
index 173660df7b5..ddf111cb863 100644
--- a/main/cond.html
+++ b/main/cond.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -776,7 +776,7 @@ <h2>Invariants of torch.ops.higher_order.cond<a class="headerlink" href="#invari
 <h3>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h3>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch._higher_order_ops.cond.cond">
-<span class="sig-prename descclassname"><span class="pre">torch._higher_order_ops.cond.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_higher_order_ops/cond.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._higher_order_ops.cond.cond" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch._higher_order_ops.cond.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_higher_order_ops/cond.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._higher_order_ops.cond.cond" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conditionally applies <cite>true_fn</cite> or <cite>false_fn</cite>.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/config_mod.html b/main/config_mod.html
index 57b95e6d43c..dab7cdd7cb9 100644
--- a/main/config_mod.html
+++ b/main/config_mod.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="torch-config"></span><h1>torch.__config__<a class="headerlink" href="#module-torch.__config__" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__config__.show">
-<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">show</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__config__.html#show"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__config__.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.show" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">show</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__config__.html#show"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__config__.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.show" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a human-readable string with descriptions of the
 configuration of PyTorch.</p>
 <dl class="field-list simple">
@@ -622,7 +622,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__config__.parallel_info">
-<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">parallel_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__config__.html#parallel_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__config__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.parallel_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">parallel_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__config__.html#parallel_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__config__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.parallel_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns detailed string with parallelization settings</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/cpp_extension.html b/main/cpp_extension.html
index 698739e666e..f444640c4f1 100644
--- a/main/cpp_extension.html
+++ b/main/cpp_extension.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extension" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.CppExtension">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">CppExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CppExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_extension.py#L977"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.CppExtension" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">CppExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CppExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L977"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.CppExtension" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> for C++.</p>
 <p>Convenience method that creates a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> with the
 bare minimum (but often sufficient) arguments to build a C++ extension.</p>
@@ -655,7 +655,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.CUDAExtension">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">CUDAExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CUDAExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_extension.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.CUDAExtension" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">CUDAExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CUDAExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.CUDAExtension" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> for CUDA/C++.</p>
 <p>Convenience method that creates a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> with the
 bare minimum (but often sufficient) arguments to build a CUDA/C++
@@ -760,7 +760,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.BuildExtension">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">BuildExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#BuildExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_extension.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.BuildExtension" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">BuildExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#BuildExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.BuildExtension" title="Permalink to this definition">¶</a></dt>
 <dd><p>A custom <code class="xref py py-mod docutils literal notranslate"><span class="pre">setuptools</span></code> build extension .</p>
 <p>This <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.build_ext</span></code> subclass takes care of passing the
 minimum required compiler flags (e.g. <code class="docutils literal notranslate"><span class="pre">-std=c++17</span></code>) as well as mixed
@@ -787,7 +787,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.load">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cuda_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_ldflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_include_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_python_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_standalone</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_intermediates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_extension.py#L1322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cuda_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_ldflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_include_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_python_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_standalone</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_intermediates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a PyTorch C++ extension just-in-time (JIT).</p>
 <p>To load an extension, a Ninja build file is emitted, which is used to
 compile the given sources into a dynamic library. This library is
@@ -873,7 +873,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.load_inline">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">load_inline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpp_sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda_sources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">functions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cuda_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_ldflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_include_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_python_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_pytorch_error_handling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_intermediates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_pch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load_inline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_extension.py#L1608"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.load_inline" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">load_inline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpp_sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda_sources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">functions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cuda_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_ldflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_include_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_python_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_pytorch_error_handling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_intermediates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_pch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load_inline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1608"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.load_inline" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a PyTorch C++ extension just-in-time (JIT) from string sources.</p>
 <p>This function behaves exactly like <a class="reference internal" href="#torch.utils.cpp_extension.load" title="torch.utils.cpp_extension.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">load()</span></code></a>, but takes its sources as
 strings rather than filenames. These strings are stored to files in the
@@ -952,7 +952,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.include_paths">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">include_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#include_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_extension.py#L1238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.include_paths" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">include_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#include_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.include_paths" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the include paths required to build a C++ or CUDA or SYCL extension.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -969,7 +969,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.get_compiler_abi_compatibility_and_version">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">get_compiler_abi_compatibility_and_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">compiler</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#get_compiler_abi_compatibility_and_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_extension.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.get_compiler_abi_compatibility_and_version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">get_compiler_abi_compatibility_and_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">compiler</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#get_compiler_abi_compatibility_and_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.get_compiler_abi_compatibility_and_version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine if the given compiler is ABI-compatible with PyTorch alongside its version.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -988,13 +988,13 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.verify_ninja_availability">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">verify_ninja_availability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#verify_ninja_availability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_extension.py#L1972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.verify_ninja_availability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">verify_ninja_availability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#verify_ninja_availability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.verify_ninja_availability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raise <code class="docutils literal notranslate"><span class="pre">RuntimeError</span></code> if <a class="reference external" href="https://ninja-build.org/">ninja</a> build system is not available on the system, does nothing otherwise.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.is_ninja_available">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">is_ninja_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#is_ninja_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_extension.py#L1962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.is_ninja_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">is_ninja_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#is_ninja_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.is_ninja_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return <code class="docutils literal notranslate"><span class="pre">True</span></code> if the <a class="reference external" href="https://ninja-build.org/">ninja</a> build system is available on the system, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
 </dd></dl>
 
diff --git a/main/cpp_index.html b/main/cpp_index.html
index 40665d6d44a..77509bb115f 100644
--- a/main/cpp_index.html
+++ b/main/cpp_index.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/cpu.html b/main/cpu.html
index 157c7b2b08b..bac36fabcb4 100644
--- a/main/cpu.html
+++ b/main/cpu.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/cuda._sanitizer.html b/main/cuda._sanitizer.html
index c0ca34fcf0b..fbaf0b61301 100644
--- a/main/cuda._sanitizer.html
+++ b/main/cuda._sanitizer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -712,7 +712,7 @@ <h2>Usage<a class="headerlink" href="#usage" title="Permalink to this heading">
 <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda._sanitizer.enable_cuda_sanitizer">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda._sanitizer.</span></span><span class="sig-name descname"><span class="pre">enable_cuda_sanitizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/_sanitizer.html#enable_cuda_sanitizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/_sanitizer.py#L651"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda._sanitizer.enable_cuda_sanitizer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda._sanitizer.</span></span><span class="sig-name descname"><span class="pre">enable_cuda_sanitizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/_sanitizer.html#enable_cuda_sanitizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/_sanitizer.py#L651"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda._sanitizer.enable_cuda_sanitizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable CUDA Sanitizer.</p>
 <p>The sanitizer will begin to analyze low-level CUDA calls invoked by torch functions
 for synchronization errors. All data races found will be printed to the standard
diff --git a/main/cuda.html b/main/cuda.html
index 41f4cdd282f..8a7bcf1be40 100644
--- a/main/cuda.html
+++ b/main/cuda.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -901,7 +901,7 @@ <h2>Graphs (beta)<a class="headerlink" href="#graphs-beta" title="Permalink to t
 </table>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.use_mem_pool">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">use_mem_pool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#use_mem_pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.use_mem_pool" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">use_mem_pool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#use_mem_pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.use_mem_pool" title="Permalink to this definition">¶</a></dt>
 <dd><p>A context manager that routes allocations to a given pool.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/cuda.tunable.html b/main/cuda.tunable.html
index eeeb69e8e70..a27ccbcdca0 100644
--- a/main/cuda.tunable.html
+++ b/main/cuda.tunable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -722,7 +722,7 @@ <h3>Tuning Context<a class="headerlink" href="#tuning-context" title="Permalink
 <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.enable">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.enable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the big on/off switch for all TunableOp implementations.</p>
 <dl class="field-list simple">
 </dl>
@@ -730,7 +730,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.is_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.is_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.is_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether the TunableOp feature is enabled.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -741,7 +741,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.tuning_enable">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tuning_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tuning_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tuning_enable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tuning_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tuning_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tuning_enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable tuning of TunableOp implementations.</p>
 <p>When enabled, if a tuned entry isn’t found, run the tuning step and record
 the entry.</p>
@@ -751,7 +751,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.tuning_is_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tuning_is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tuning_is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tuning_is_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tuning_is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tuning_is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tuning_is_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether TunableOp implementations can be tuned.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -762,7 +762,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.record_untuned_enable">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">record_untuned_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#record_untuned_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.record_untuned_enable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">record_untuned_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#record_untuned_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.record_untuned_enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable recording untuned of TunableOp perations for offline tuning.</p>
 <p>When enabled, if a tuned entry isn’t found, write it to the untuned file.</p>
 <dl class="field-list simple">
@@ -771,7 +771,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.record_untuned_is_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">record_untuned_is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#record_untuned_is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.record_untuned_is_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">record_untuned_is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#record_untuned_is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.record_untuned_is_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether TunableOp operations are recorded for offline tuning.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -782,7 +782,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.set_max_tuning_duration">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_max_tuning_duration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">duration</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_max_tuning_duration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_max_tuning_duration" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_max_tuning_duration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">duration</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_max_tuning_duration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_max_tuning_duration" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set max time in milliseconds to spend tuning a given solution.</p>
 <p>If both max tuning duration and iterations are set, the smaller of the two
 will be honored. At minimum 1 tuning iteration will always be run.</p>
@@ -792,7 +792,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_max_tuning_duration">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_max_tuning_duration</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_max_tuning_duration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_max_tuning_duration" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_max_tuning_duration</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_max_tuning_duration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_max_tuning_duration" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get max time to spend tuning a given solution.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -803,7 +803,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.set_max_tuning_iterations">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_max_tuning_iterations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_max_tuning_iterations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_max_tuning_iterations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_max_tuning_iterations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_max_tuning_iterations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_max_tuning_iterations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set max number of iterations to spend tuning a given solution.</p>
 <p>If both max tuning duration and iterations are set, the smaller of the two
 will be honored. At minimum 1 tuning iteration will always be run.</p>
@@ -813,7 +813,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_max_tuning_iterations">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_max_tuning_iterations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_max_tuning_iterations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_max_tuning_iterations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_max_tuning_iterations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_max_tuning_iterations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_max_tuning_iterations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get max iterations to spend tuning a given solution.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -824,7 +824,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.set_filename">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_filename</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">insert_device_ordinal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_filename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_filename" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_filename</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">insert_device_ordinal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_filename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_filename" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the filename to use for input/output of tuning results.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">insert_device_ordinal</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code> then the current device ordinal
 will be added to the given filename automatically. This can be used in a
@@ -835,7 +835,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_filename">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_filename</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_filename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_filename" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_filename</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_filename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_filename" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the results filename.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -846,7 +846,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_results">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_results</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_results" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_results</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_results" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all TunableOp results.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -857,7 +857,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_validators">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_validators</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_validators"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_validators" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_validators</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_validators"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_validators" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the TunableOp validators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -868,7 +868,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.write_file_on_exit">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">write_file_on_exit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#write_file_on_exit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.write_file_on_exit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">write_file_on_exit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#write_file_on_exit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.write_file_on_exit" title="Permalink to this definition">¶</a></dt>
 <dd><p>During Tuning Context destruction, write file to disk.</p>
 <p>This is useful as a final flush of your results to disk if your application
 terminates as result of normal operation or an error. Manual flushing of
@@ -879,7 +879,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.write_file">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">write_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#write_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.write_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">write_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#write_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.write_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write results to a CSV file.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">filename</span></code> is not given, <code class="docutils literal notranslate"><span class="pre">get_filename()</span></code> is called.</p>
 <dl class="field-list simple">
@@ -891,7 +891,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.read_file">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">read_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#read_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.read_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">read_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#read_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.read_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Read results from a TunableOp CSV file.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">filename</span></code> is not given, <code class="docutils literal notranslate"><span class="pre">get_filename()</span></code> is called.</p>
 <dl class="field-list simple">
@@ -903,7 +903,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.tune_gemm_in_file">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tune_gemm_in_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tune_gemm_in_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tune_gemm_in_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tune_gemm_in_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tune_gemm_in_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tune_gemm_in_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>tune GEMM in file.</p>
 <dl class="field-list simple">
 </dl>
@@ -911,7 +911,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.mgpu_tune_gemm_in_file">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">mgpu_tune_gemm_in_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename_pattern</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_gpus</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#mgpu_tune_gemm_in_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.mgpu_tune_gemm_in_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">mgpu_tune_gemm_in_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename_pattern</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_gpus</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#mgpu_tune_gemm_in_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.mgpu_tune_gemm_in_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Process one or more files and distribute work over one or more GPUs.</p>
 <dl class="field-list simple">
 </dl>
@@ -919,7 +919,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.set_rotating_buffer_size">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_rotating_buffer_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">buffer_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_rotating_buffer_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_rotating_buffer_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_rotating_buffer_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">buffer_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_rotating_buffer_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_rotating_buffer_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set rotating buffer size to this value in MB, if the buffer size is greater than zero.</p>
 <p>If less than zero, query L2 cache size. If equal to zero, means deactivate rotating buffer.</p>
 <dl class="field-list simple">
@@ -928,7 +928,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_rotating_buffer_size">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_rotating_buffer_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_rotating_buffer_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/tunable.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_rotating_buffer_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_rotating_buffer_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_rotating_buffer_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_rotating_buffer_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the rotating buffer size in kilobytes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/cuda_environment_variables.html b/main/cuda_environment_variables.html
index 94a2cc42f40..33a5407859a 100644
--- a/main/cuda_environment_variables.html
+++ b/main/cuda_environment_variables.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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diff --git a/main/cudnn_persistent_rnn.html b/main/cudnn_persistent_rnn.html
index 2a49c2b64f0..d9f3863d97d 100644
--- a/main/cudnn_persistent_rnn.html
+++ b/main/cudnn_persistent_rnn.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/cudnn_rnn_determinism.html b/main/cudnn_rnn_determinism.html
index 178c8caa361..60f4a03997f 100644
--- a/main/cudnn_rnn_determinism.html
+++ b/main/cudnn_rnn_determinism.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/data.html b/main/data.html
index 69ff55c1051..ee82459311d 100644
--- a/main/data.html
+++ b/main/data.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -976,7 +976,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.DataLoader">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">DataLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">worker_init_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiprocessing_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefetch_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataloader.html#DataLoader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/dataloader.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.DataLoader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">DataLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">worker_init_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiprocessing_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefetch_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataloader.html#DataLoader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataloader.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.DataLoader" title="Permalink to this definition">¶</a></dt>
 <dd><p>Data loader combines a dataset and a sampler, and provides an iterable over the given dataset.</p>
 <p>The <a class="reference internal" href="#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> supports both map-style and
 iterable-style datasets with single- or multi-process loading, customizing
@@ -1077,7 +1077,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.Dataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/dataset.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Dataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Dataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>An abstract class representing a <a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a>.</p>
 <p>All datasets that represent a map from keys to data samples should subclass
 it. All subclasses should overwrite <code class="xref py py-meth docutils literal notranslate"><span class="pre">__getitem__()</span></code>, supporting fetching a
@@ -1098,7 +1098,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.IterableDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">IterableDataset</span></span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#IterableDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/dataset.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.IterableDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">IterableDataset</span></span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#IterableDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.IterableDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>An iterable Dataset.</p>
 <p>All datasets that represent an iterable of data samples should subclass it.
 Such form of datasets is particularly useful when data come from a stream.</p>
@@ -1200,7 +1200,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.TensorDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">TensorDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#TensorDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/dataset.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.TensorDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">TensorDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#TensorDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.TensorDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataset wrapping tensors.</p>
 <p>Each sample will be retrieved by indexing tensors along the first dimension.</p>
 <dl class="field-list simple">
@@ -1212,7 +1212,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.StackDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">StackDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#StackDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/dataset.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.StackDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">StackDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#StackDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.StackDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataset as a stacking of multiple datasets.</p>
 <p>This class is useful to assemble different parts of complex input data, given as datasets.</p>
 <p class="rubric">Example</p>
@@ -1236,7 +1236,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.ConcatDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">ConcatDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#ConcatDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/dataset.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.ConcatDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">ConcatDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#ConcatDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.ConcatDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataset as a concatenation of multiple datasets.</p>
 <p>This class is useful to assemble different existing datasets.</p>
 <dl class="field-list simple">
@@ -1248,7 +1248,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.ChainDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">ChainDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#ChainDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/dataset.py#L351"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.ChainDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">ChainDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#ChainDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L351"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.ChainDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataset for chaining multiple <a class="reference internal" href="#torch.utils.data.IterableDataset" title="torch.utils.data.IterableDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">IterableDataset</span></code></a> s.</p>
 <p>This class is useful to assemble different existing dataset streams. The
 chaining operation is done on-the-fly, so concatenating large-scale
@@ -1262,7 +1262,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.Subset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Subset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Subset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/dataset.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Subset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Subset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Subset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Subset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Subset of a dataset at specified indices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1276,7 +1276,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data._utils.collate.collate">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data._utils.collate.</span></span><span class="sig-name descname"><span class="pre">collate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#collate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/_utils/collate.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data._utils.collate.collate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data._utils.collate.</span></span><span class="sig-name descname"><span class="pre">collate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#collate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/_utils/collate.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data._utils.collate.collate" title="Permalink to this definition">¶</a></dt>
 <dd><p>General collate function that handles collection type of element within each batch.</p>
 <p>The function also opens function registry to deal with specific element types. <cite>default_collate_fn_map</cite>
 provides default collate functions for tensors, numpy arrays, numbers and strings.</p>
@@ -1311,7 +1311,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data.default_collate">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">default_collate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#default_collate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/_utils/collate.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.default_collate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">default_collate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#default_collate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/_utils/collate.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.default_collate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Take in a batch of data and put the elements within the batch into a tensor with an additional outer dimension - batch size.</p>
 <p>The exact output type can be a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>, a <cite>Sequence</cite> of <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>, a
 Collection of <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>, or left unchanged, depending on the input type.
@@ -1377,7 +1377,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data.default_convert">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">default_convert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#default_convert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/_utils/collate.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.default_convert" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">default_convert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#default_convert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/_utils/collate.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.default_convert" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert each NumPy array element into a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>.</p>
 <p>If the input is a <cite>Sequence</cite>, <cite>Collection</cite>, or <cite>Mapping</cite>, it tries to convert each element inside to a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>.
 If the input is not an NumPy array, it is left unchanged.
@@ -1412,7 +1412,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data.get_worker_info">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">get_worker_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/worker.html#get_worker_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/_utils/worker.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.get_worker_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">get_worker_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/worker.html#get_worker_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/_utils/worker.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.get_worker_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the information about the current
 <a class="reference internal" href="#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> iterator worker process.</p>
 <p>When called in a worker, this returns an object guaranteed to have the
@@ -1446,7 +1446,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data.random_split">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">random_split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator=&lt;torch._C.Generator</span> <span class="pre">object&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#random_split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/dataset.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.random_split" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">random_split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator=&lt;torch._C.Generator</span> <span class="pre">object&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#random_split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.random_split" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly split a dataset into non-overlapping new datasets of given lengths.</p>
 <p>If a list of fractions that sum up to 1 is given,
 the lengths will be computed automatically as
@@ -1478,7 +1478,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.Sampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#Sampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/sampler.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Sampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#Sampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Sampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for all Samplers.</p>
 <p>Every Sampler subclass has to provide an <code class="xref py py-meth docutils literal notranslate"><span class="pre">__iter__()</span></code> method, providing a
 way to iterate over indices or lists of indices (batches) of dataset elements,
@@ -1525,7 +1525,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.SequentialSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">SequentialSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SequentialSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/sampler.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.SequentialSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">SequentialSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SequentialSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.SequentialSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Samples elements sequentially, always in the same order.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1536,7 +1536,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.RandomSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">RandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#RandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/sampler.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.RandomSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">RandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#RandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.RandomSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Samples elements randomly. If without replacement, then sample from a shuffled dataset.</p>
 <p>If with replacement, then user can specify <code class="xref py py-attr docutils literal notranslate"><span class="pre">num_samples</span></code> to draw.</p>
 <dl class="field-list simple">
@@ -1553,7 +1553,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.SubsetRandomSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">SubsetRandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SubsetRandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/sampler.py#L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.SubsetRandomSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">SubsetRandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SubsetRandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.SubsetRandomSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Samples elements randomly from a given list of indices, without replacement.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1567,7 +1567,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.WeightedRandomSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">WeightedRandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#WeightedRandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/sampler.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.WeightedRandomSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">WeightedRandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#WeightedRandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.WeightedRandomSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Samples elements from <code class="docutils literal notranslate"><span class="pre">[0,..,len(weights)-1]</span></code> with given probabilities (weights).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1592,7 +1592,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.BatchSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">BatchSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sampler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#BatchSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/sampler.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.BatchSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">BatchSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sampler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#BatchSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.BatchSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wraps another sampler to yield a mini-batch of indices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1615,7 +1615,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.distributed.DistributedSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.distributed.</span></span><span class="sig-name descname"><span class="pre">DistributedSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_replicas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/distributed.html#DistributedSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/data/distributed.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.distributed.DistributedSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.distributed.</span></span><span class="sig-name descname"><span class="pre">DistributedSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_replicas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/distributed.html#DistributedSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/distributed.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.distributed.DistributedSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sampler that restricts data loading to a subset of the dataset.</p>
 <p>It is especially useful in conjunction with
 <a class="reference internal" href="generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel" title="torch.nn.parallel.DistributedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataParallel</span></code></a>. In such a case, each
diff --git a/main/ddp_comm_hooks.html b/main/ddp_comm_hooks.html
index 47d232333b8..c8c4999dd0a 100644
--- a/main/ddp_comm_hooks.html
+++ b/main/ddp_comm_hooks.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -710,7 +710,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 The input <code class="docutils literal notranslate"><span class="pre">bucket</span></code> is a <a class="reference internal" href="#torch.distributed.GradBucket" title="torch.distributed.GradBucket"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.distributed.GradBucket</span></code></a> object.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.allreduce_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">allreduce_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#allreduce_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.allreduce_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">allreduce_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#allreduce_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.allreduce_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call <code class="docutils literal notranslate"><span class="pre">allreduce</span></code> using <code class="docutils literal notranslate"><span class="pre">GradBucket</span></code> tensors.</p>
 <p>Once gradient tensors are aggregated across all workers, its <code class="docutils literal notranslate"><span class="pre">then</span></code>
 callback takes the mean and returns the result.</p>
@@ -734,7 +734,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">fp16_compress_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#fp16_compress_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">fp16_compress_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#fp16_compress_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compress by casting <code class="docutils literal notranslate"><span class="pre">GradBucket</span></code> to <code class="docutils literal notranslate"><span class="pre">torch.float16</span></code> divided by process group size.</p>
 <p>This DDP communication hook implements a simple gradient compression
 approach that casts <code class="docutils literal notranslate"><span class="pre">GradBucket</span></code> tensor to half-precision floating-point format (<code class="docutils literal notranslate"><span class="pre">torch.float16</span></code>)
@@ -756,7 +756,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">bf16_compress_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#bf16_compress_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">bf16_compress_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#bf16_compress_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Warning: This API is experimental, and it requires NCCL version later than 2.9.6.</p>
 <p>This DDP communication hook implements a simple gradient compression
 approach that casts <code class="docutils literal notranslate"><span class="pre">GradBucket</span></code> tensor to half-precision
@@ -781,7 +781,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 which can be combined with other communication hooks.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_wrapper">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">fp16_compress_wrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#fp16_compress_wrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_wrapper" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">fp16_compress_wrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#fp16_compress_wrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_wrapper" title="Permalink to this definition">¶</a></dt>
 <dd><p>Cast input tensor to <code class="docutils literal notranslate"><span class="pre">torch.float16</span></code>, cast result of hook back to input dtype.</p>
 <p>This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
 floating point format (<code class="docutils literal notranslate"><span class="pre">torch.float16</span></code>), and casts the resulting tensor of the given hook back to
@@ -803,7 +803,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_wrapper">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">bf16_compress_wrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#bf16_compress_wrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_wrapper" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">bf16_compress_wrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#bf16_compress_wrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_wrapper" title="Permalink to this definition">¶</a></dt>
 <dd><p>Warning: This API is experimental, and it requires NCCL version later than 2.9.6.</p>
 <p>This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
 <cite>Brain floating point format &lt;https://en.wikipedia.org/wiki/Bfloat16_floating-point_format&gt; `_  (``torch.bfloat16`</cite>),
@@ -836,7 +836,7 @@ <h2>PowerSGD Communication Hook<a class="headerlink" href="#powersgd-communicati
 <h3>PowerSGD State<a class="headerlink" href="#powersgd-state" title="Permalink to this heading">¶</a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">PowerSGDState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">matrix_approximation_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_powerSGD_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_compression_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_error_feedback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orthogonalization_epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compression_stats_logging_frequency</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_tensors_with_same_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">PowerSGDState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">matrix_approximation_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_powerSGD_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_compression_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_error_feedback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orthogonalization_epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compression_stats_logging_frequency</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_tensors_with_same_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState" title="Permalink to this definition">¶</a></dt>
 <dd><p>Store both the algorithm’s hyperparameters and internal state for all gradients during training.</p>
 <p>Particularly, <code class="docutils literal notranslate"><span class="pre">matrix_approximation_rank</span></code> and <code class="docutils literal notranslate"><span class="pre">start_powerSGD_iter</span></code> are the main hyperparameters that should be tuned by the user.
 For performance, we suggest to keep binary hyperparameters <code class="docutils literal notranslate"><span class="pre">use_error_feedback</span></code> and <code class="docutils literal notranslate"><span class="pre">warm_start</span></code> on.</p>
@@ -888,7 +888,7 @@ <h3>PowerSGD Hooks<a class="headerlink" href="#powersgd-hooks" title="Permalink
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.powerSGD_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">powerSGD_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#powerSGD_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.powerSGD_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">powerSGD_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#powerSGD_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.powerSGD_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement PowerSGD algorithm.</p>
 <p>This DDP communication hook implements PowerSGD gradient compression
 algorithm described in the <a class="reference external" href="https://arxiv.org/abs/1905.13727">paper</a>.
@@ -953,7 +953,7 @@ <h3>PowerSGD Hooks<a class="headerlink" href="#powersgd-hooks" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.batched_powerSGD_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">batched_powerSGD_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#batched_powerSGD_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.batched_powerSGD_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">batched_powerSGD_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#batched_powerSGD_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.batched_powerSGD_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement simplified PowerSGD algorithm.</p>
 <p>This DDP communication hook implements a simplified PowerSGD gradient compression
 algorithm described in the <a class="reference external" href="https://arxiv.org/abs/1905.13727">paper</a>.
@@ -1021,7 +1021,7 @@ <h2>Debugging Communication Hooks<a class="headerlink" href="#debugging-communic
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.noop_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.</span></span><span class="sig-name descname"><span class="pre">noop_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.html#noop_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.noop_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.</span></span><span class="sig-name descname"><span class="pre">noop_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.html#noop_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.noop_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a future that wraps the input, so it is a no-op that does not incur any communication overheads.</p>
 <p>This hook should <strong>only</strong> be used for headroom analysis of allreduce optimization,
 instead of the normal gradient synchronization.
@@ -1059,12 +1059,12 @@ <h2>Checkpointing of Communication Hooks<a class="headerlink" href="#checkpointi
 <p><a class="reference internal" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState" title="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState"><code class="xref py py-class docutils literal notranslate"><span class="pre">PowerSGDState</span></code></a> has <code class="docutils literal notranslate"><span class="pre">__setstate__</span></code> and <code class="docutils literal notranslate"><span class="pre">__getstate__</span></code> implemented and can be used as a reference.</p>
 <dl class="py class">
 <dt class="sig sig-object py">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">PowerSGDState</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">PowerSGDState</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__getstate__">
-<span class="sig-name descname"><span class="pre">__getstate__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState.__getstate__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__getstate__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__getstate__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState.__getstate__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__getstate__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a <code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">Any]</span></code> which will be pickled and saved.</p>
 <p><code class="docutils literal notranslate"><span class="pre">process_group</span></code> is not serializable and excluded from
 a returned state.</p>
@@ -1072,7 +1072,7 @@ <h2>Checkpointing of Communication Hooks<a class="headerlink" href="#checkpointi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__setstate__">
-<span class="sig-name descname"><span class="pre">__setstate__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState.__setstate__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L291"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__setstate__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__setstate__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState.__setstate__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L291"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__setstate__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Take a provided <code class="docutils literal notranslate"><span class="pre">state</span></code> and set to this <code class="docutils literal notranslate"><span class="pre">PowerSGDState</span></code> instance.</p>
 <p><code class="docutils literal notranslate"><span class="pre">process_group</span></code> is set to default.</p>
 </dd></dl>
diff --git a/main/debugging_environment_variables.html b/main/debugging_environment_variables.html
index 59512911583..9fdffc96691 100644
--- a/main/debugging_environment_variables.html
+++ b/main/debugging_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/deploy.html b/main/deploy.html
index 70284c3d4e5..a75622e175e 100644
--- a/main/deploy.html
+++ b/main/deploy.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/deterministic.html b/main/deterministic.html
index db32651d5fb..29c50724b70 100644
--- a/main/deterministic.html
+++ b/main/deterministic.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/distributed.algorithms.join.html b/main/distributed.algorithms.join.html
index 21d6b198b14..f8974231836 100644
--- a/main/distributed.algorithms.join.html
+++ b/main/distributed.algorithms.join.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 <a class="reference external" href="https://pytorch.org/tutorials/advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.Join">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">Join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">throw_on_early_termination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/join.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">Join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">throw_on_early_termination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join" title="Permalink to this definition">¶</a></dt>
 <dd><p>This class defines the generic join context manager, which allows custom hooks to be called after a process joins.</p>
 <p>These hooks should shadow the
 collective communications of non-joined processes to prevent hanging and
@@ -677,7 +677,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.Join.notify_join_context">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">notify_join_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join.notify_join_context"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/join.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join.notify_join_context" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">notify_join_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join.notify_join_context"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join.notify_join_context" title="Permalink to this definition">¶</a></dt>
 <dd><p>Notifies the join context manager that the calling process has not yet joined.</p>
 <p>Then, if <code class="docutils literal notranslate"><span class="pre">throw_on_early_termination=True</span></code>, checks if uneven inputs have been detected
 (i.e. if one process has already joined) and throws an exception if so.</p>
@@ -705,7 +705,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.Joinable">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">Joinable</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/join.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">Joinable</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable" title="Permalink to this definition">¶</a></dt>
 <dd><p>This defines an abstract base class for joinable classes.</p>
 <p>A joinable class
 (inheriting from <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a>) should implement <a class="reference internal" href="#torch.distributed.algorithms.Joinable.join_hook" title="torch.distributed.algorithms.Joinable.join_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">join_hook()</span></code></a>,
@@ -722,7 +722,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.Joinable.join_hook">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/join.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable.join_hook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable.join_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a <a class="reference internal" href="#torch.distributed.algorithms.JoinHook" title="torch.distributed.algorithms.JoinHook"><code class="xref py py-class docutils literal notranslate"><span class="pre">JoinHook</span></code></a> instance for the given <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -747,7 +747,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.JoinHook">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">JoinHook</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/join.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">JoinHook</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook" title="Permalink to this definition">¶</a></dt>
 <dd><p>This defines a join hook, which provides two entry points in the join context manager.</p>
 <p>Entry points : a main hook, which is called repeatedly while there exists a non-joined
 process, and a post-hook, which is called once all processes have joined.</p>
@@ -756,7 +756,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 <code class="docutils literal notranslate"><span class="pre">post_hook()</span></code> as appropriate.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.JoinHook.main_hook">
-<span class="sig-name descname"><span class="pre">main_hook</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.main_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/join.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.main_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">main_hook</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.main_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.main_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call this hook while there exists a non-joined process to shadow collective communications in a training iteration.</p>
 <p>Training iteration i.e., in one forward pass, backward pass, and optimizer step.</p>
 <dl class="field-list simple">
@@ -765,7 +765,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.JoinHook.post_hook">
-<span class="sig-name descname"><span class="pre">post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_last_joiner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/algorithms/join.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_last_joiner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call hook after all processes have joined.</p>
 <p>It is passed an additional <code class="docutils literal notranslate"><span class="pre">bool</span></code> argument <code class="docutils literal notranslate"><span class="pre">is_last_joiner</span></code>, which indicates if the rank is one of the last to join.</p>
 <dl class="field-list simple">
diff --git a/main/distributed.checkpoint.html b/main/distributed.checkpoint.html
index b4f7e2c5cc1..12308a7bda3 100644
--- a/main/distributed.checkpoint.html
+++ b/main/distributed.checkpoint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -626,7 +626,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </ul>
 <span class="target" id="module-torch.distributed.checkpoint"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_saver.save">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict_saver.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_saver.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save a distributed model in SPMD style.</p>
 <p>This function is different from <code class="docutils literal notranslate"><span class="pre">torch.save()</span></code> as it handles
 <code class="docutils literal notranslate"><span class="pre">ShardedTensor</span></code> , and <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> by having each rank only save their local shards.</p>
@@ -671,6 +671,9 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 planner will be used. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
 <li><p><strong>process_group</strong> (<em>Optional</em><em>[</em><em>ProcessGroup</em><em>]</em>) – ProcessGroup to be used for cross-rank synchronization.
 (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
+<li><p><strong>no_dist</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, this function will assume the intent is to load
+a checkpoint without using cross-rank synchronization.
+(Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -707,7 +710,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_saver.async_save">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">async_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#async_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict_saver.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.async_save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">async_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#async_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_saver.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.async_save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Asynchronous version of <code class="docutils literal notranslate"><span class="pre">save</span></code>. This code first de-stages the state_dict on to the
 staging storage (defaults to CPU memory), and then calls the <cite>save</cite> in a separate thread.</p>
 <div class="admonition warning">
@@ -761,7 +764,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_saver.save_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict_saver.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_saver.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>This method is deprecated. Please switch to ‘save’.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -772,7 +775,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_loader.load">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict_loader.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_loader.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a distributed <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> in SPMD style.</p>
 <p>Each rank will try to read the least amount of data necessary
 to fullfill the requested <cite>state_dict</cite>. When loading <code class="xref py py-class docutils literal notranslate"><span class="pre">ShardedTensor</span></code>
@@ -810,6 +813,8 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 planner will be used. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
 <li><p><strong>process_group</strong> (<em>Optional</em><em>[</em><em>ProcessGroup</em><em>]</em>) – ProcessGroup to be used for cross-rank synchronization.
 (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
+<li><p><strong>no_dist</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, this function will assume the intent is to load
+a checkpoint without using cross-rank synchronization. (Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -853,7 +858,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_loader.load_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict_loader.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_loader.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>This method is deprecated. Please switch to ‘load’.</p>
 <dl class="field-list simple">
 </dl>
@@ -862,7 +867,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>The following module is also useful for additional customization of the staging mechanisms used for asynchronous checkpointing (<cite>torch.distributed.checkpoint.async_save</cite>):</p>
 <span class="target" id="module-torch.distributed.checkpoint.staging"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">AsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/staging.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">AsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager" title="Permalink to this definition">¶</a></dt>
 <dd><p>This protocol is meant to provide customization and extensibility for dcp.async_save, allowing users
 to customize how data is staged previous to executing the usual dcp.save path in parallel.
 The expected order of operations (concretely defined in <cite>torch.distributed.state_dict_saver.async_save</cite>)
@@ -901,7 +906,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager.stage">
-<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/staging.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.stage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.stage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a “staged” copy of <cite>state_dict</cite>. The expectation of the staged copy is that it is
 innoculated from any updates incurred after the stage call is complete.</p>
 <dl class="field-list simple">
@@ -913,7 +918,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager.synchronize_staging">
-<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/staging.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
 <dd><p>In the case <cite>stage</cite> is async in some way, this method should be called to ensure staging
 is complete and it is safe to begin modifying the original <cite>state_dict</cite></p>
 <dl class="field-list simple">
@@ -924,7 +929,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.BlockingAsyncStager">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">BlockingAsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_staged_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/staging.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">BlockingAsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_staged_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager" title="Permalink to this definition">¶</a></dt>
 <dd><p>An implementation of AsyncStager which stages the state_dict on CPU RAM and blocks until the copy is complete.
 This implementation also provides an option to optimize stage latency using pinned memory.</p>
 <p>N.B. synchronize_staging is a no-op in this case.</p>
@@ -932,7 +937,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.BlockingAsyncStager.stage">
-<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/staging.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.stage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.stage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of <cite>state_dict</cite> on the CPU.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -943,7 +948,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.BlockingAsyncStager.synchronize_staging">
-<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/staging.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
 <dd><p>No-op function, since staging is blocking.</p>
 <dl class="field-list simple">
 </dl>
@@ -955,11 +960,11 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 .. automodule:: torch.distributed.checkpoint.stateful</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.stateful.</span></span><span class="sig-name descname"><span class="pre">Stateful</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/stateful.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.stateful.</span></span><span class="sig-name descname"><span class="pre">Stateful</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/stateful.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful" title="Permalink to this definition">¶</a></dt>
 <dd><p>Stateful protocol for objects that can be checkpointed and restored.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/stateful.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/stateful.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Restore the object’s state from the provided state_dict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -970,7 +975,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/stateful.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/stateful.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Objects should return their state_dict representation as a dictionary.
 The output of this function will be checkpointed, and later restored in
 <cite>load_state_dict()</cite>.</p>
@@ -995,7 +1000,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>The following types define the IO interface used during checkpoint:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageReader</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageReader</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader" title="Permalink to this definition">¶</a></dt>
 <dd><p>Interface used by <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code> to read from storage.</p>
 <p>One StorageReader instance acts as both the coordinator and the follower
 in a distributed checkpoint. As part of initialization, each instance
@@ -1011,7 +1016,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </ol>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.prepare_global_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform centralized planning of storage loading.</p>
 <p>This method is only called on the coordinator instance.</p>
 <p>While this method can produce a completely different plan, the preferred
@@ -1031,7 +1036,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.prepare_local_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform storage-specific local planning.</p>
 <p>While this method can produce a completely different plan, the recommended
 way is to store storage specific data in LoadPlan::storage_data.</p>
@@ -1050,7 +1055,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.read_data">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_data" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Read all items from <code class="docutils literal notranslate"><span class="pre">plan</span></code> using <code class="docutils literal notranslate"><span class="pre">planner</span></code> to resolve the data.</p>
 <p>A subclass should call <code class="docutils literal notranslate"><span class="pre">LoadPlanner::load_bytes</span></code> to deserialize a BytesIO
 object into the right place.</p>
@@ -1076,7 +1081,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.read_metadata">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_metadata" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_metadata" title="Permalink to this definition">¶</a></dt>
 <dd><p>Read the checkpoint metadata.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1090,7 +1095,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.reset">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.reset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calls to indicates a brand new checkpoint read is going to happen.
 A checkpoint_id may be present if users set the checkpoint_id for
 this checkpoint read. The meaning of the checkpiont_id is
@@ -1108,7 +1113,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.set_up_storage_reader">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_reader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.set_up_storage_reader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.set_up_storage_reader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_reader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.set_up_storage_reader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.set_up_storage_reader" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize this instance.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1123,7 +1128,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.validate_checkpoint_id">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the given checkpoint_id is supported by the stroage. This allow
 us to enable automatic storage selection.</p>
 <dl class="field-list simple">
@@ -1137,7 +1142,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageWriter</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageWriter</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Interface used by <code class="docutils literal notranslate"><span class="pre">save_state_dict</span></code> to write to storage.</p>
 <p>One StorageWriter instance acts as both the coordinator and the follower
 in a distributed checkpoint. As part of initialization, each instance
@@ -1153,7 +1158,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </ol>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.finish">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.finish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.finish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.finish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.finish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write the metadata and marks the current checkpoint as successful.</p>
 <p>The actual format/schema used for serializing <cite>metadata</cite> is an
 implementation detail. The only requirement is that it’s recoverable
@@ -1176,7 +1181,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.prepare_global_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform centralized planning of storage.</p>
 <p>This method is only called on the coordinator instance.</p>
 <p>While this method can produce a completely different plan, the preferred
@@ -1196,7 +1201,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.prepare_local_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform storage-specific local planning.</p>
 <p>While this method can produce a completely different plan, the recommended
 way is to store storage specific data in SavePlan::storage_data.</p>
@@ -1215,7 +1220,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.reset">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.reset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calls to indicates a brand new checkpoint write is going to happen.
 A checkpoint_id may be present if users set the checkpoint_id for
 this checkpoint write. The meaning of the checkpiont_id is
@@ -1233,7 +1238,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.set_up_storage_writer">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_writer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.set_up_storage_writer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.set_up_storage_writer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_writer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.set_up_storage_writer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.set_up_storage_writer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize this instance.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1245,7 +1250,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.storage_meta">
-<span class="sig-name descname"><span class="pre">storage_meta</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.storage_meta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.storage_meta" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">storage_meta</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.storage_meta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.storage_meta" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the storage-specific metadata. This is used to store additional information
 in a checkpoint that can be useful for providing request-level observability. StorageMeta
 is passed to the <code class="docutils literal notranslate"><span class="pre">SavePlanner</span></code> during save calls. Returns None by default.</p>
@@ -1259,7 +1264,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.validate_checkpoint_id">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the given checkpoint_id is supported by the stroage. This allow
 us to enable automatic storage selection.</p>
 <dl class="field-list simple">
@@ -1271,7 +1276,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.write_data">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">write_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.write_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/storage.py#L105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.write_data" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">write_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.write_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.write_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write all items from <code class="docutils literal notranslate"><span class="pre">plan</span></code> using <code class="docutils literal notranslate"><span class="pre">planner</span></code> to resolve the data.</p>
 <p>A subclass should call <code class="docutils literal notranslate"><span class="pre">SavePlanner::resolve_data</span></code> on each item
 from the plan to get access to the underlying object to write.</p>
@@ -1302,7 +1307,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>The following types define the planner interface used during checkpoint:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">LoadPlanner</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">LoadPlanner</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class defining the protocol used by load_state_dict to plan the load process.</p>
 <p>LoadPlanner are stateful objects that can be used to customize the whole load process.</p>
 <p>LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
@@ -1379,7 +1384,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.commit_tensor">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">commit_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.commit_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.commit_tensor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">commit_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.commit_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.commit_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call once the StorageReader finished loading data into <code class="docutils literal notranslate"><span class="pre">tensor</span></code>.</p>
 <p>The provided tensor is the same one returned by the call to <code class="docutils literal notranslate"><span class="pre">resolve_tensor</span></code>.
 This method is only needed if this LoadPlanner needs to post process <code class="docutils literal notranslate"><span class="pre">tensor</span></code> prior to
@@ -1391,7 +1396,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.create_global_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.create_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.create_global_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.create_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.create_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the global load plan and return plans for each rank.</p>
 <p>. N.B. This is called on the coordinator rank only</p>
 <dl class="field-list simple">
@@ -1403,7 +1408,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.create_local_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_local_plan</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.create_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.create_local_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_local_plan</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.create_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.create_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a LoadPlan based on state_dict and metadata provided by set_up_planner.</p>
 <p>. N.B. This is called on every rank.</p>
 <dl class="field-list simple">
@@ -1415,7 +1420,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.finish_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">central_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.finish_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.finish_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">central_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.finish_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.finish_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Accept the plan from coordinator and return final LoadPlan.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1426,7 +1431,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.load_bytes">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load_bytes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.load_bytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.load_bytes" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load_bytes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.load_bytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.load_bytes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the item described by <code class="docutils literal notranslate"><span class="pre">read_item``and</span> <span class="pre">``value</span></code>.</p>
 <p>This method is expected to modify in-place the underlying state_dict.</p>
 <p>The contents of <code class="docutils literal notranslate"><span class="pre">value</span></code> are defined by the SavePlanner used to produce
@@ -1437,7 +1442,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.resolve_bytes">
-<span class="sig-name descname"><span class="pre">resolve_bytes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.resolve_bytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.resolve_bytes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">resolve_bytes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.resolve_bytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.resolve_bytes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the BytesIO to be used by the StorageReader to load <cite>read_item</cite>.</p>
 <p>The BytesIO should alias with one on the underlying state_dict as StorageReader will replace its contents.</p>
 <dl class="field-list simple">
@@ -1449,7 +1454,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.resolve_tensor">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">resolve_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.resolve_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.resolve_tensor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">resolve_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.resolve_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.resolve_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the tensor described by <code class="docutils literal notranslate"><span class="pre">read_item</span></code> to be used by the StorageReader to load <cite>read_item</cite>.</p>
 <p>The tensor should alias with one on the underlying state_dict as StorageReader will replace its contents.
 If, for any reason, that’s not possible, the planner can use the <code class="docutils literal notranslate"><span class="pre">commit_tensor</span></code> method to copy the data
@@ -1463,7 +1468,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.set_up_planner">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize this instance to load data into <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.</p>
 <p>. N.B. This is called on every rank.</p>
 <dl class="field-list simple">
@@ -1474,21 +1479,21 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlan">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">LoadPlan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.distributed.checkpoint.ReadItem" title="torch.distributed.checkpoint.planner.ReadItem"><span class="pre">torch.distributed.checkpoint.planner.ReadItem</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">LoadPlan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.distributed.checkpoint.ReadItem" title="torch.distributed.checkpoint.planner.ReadItem"><span class="pre">torch.distributed.checkpoint.planner.ReadItem</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlan" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.ReadItem">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">ReadItem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.planner.LoadItemType</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.metadata.MetadataIndex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_offsets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.metadata.MetadataIndex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_offsets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#ReadItem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.ReadItem" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">ReadItem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.planner.LoadItemType</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.metadata.MetadataIndex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_offsets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.metadata.MetadataIndex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_offsets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#ReadItem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.ReadItem" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SavePlanner</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SavePlanner</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class defining the protocol used by save_state_dict to plan the save process.</p>
 <p>SavePlanners are stateful objects that can be used to customize the whole save process.</p>
 <p>SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
@@ -1587,7 +1592,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.create_global_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">all_plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.create_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.create_global_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">all_plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.create_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.create_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the global checkpoint plan and return the local plan of each rank.</p>
 <p>This is called on the coordinator rank only.</p>
 <dl class="field-list simple">
@@ -1599,7 +1604,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.create_local_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_local_plan</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.create_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.create_local_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_local_plan</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.create_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.create_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the save plan for the current rank.</p>
 <p>This will be aggregated and passed to create_global_plan.
 Planner specific data can be passed through SavePlan::planner_data.</p>
@@ -1613,7 +1618,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.finish_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.finish_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.finish_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.finish_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.finish_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Merge the plan created by <cite>create_local_plan</cite> and the result of <cite>create_global_plan</cite>.</p>
 <p>This is called on all ranks.</p>
 <dl class="field-list simple">
@@ -1625,7 +1630,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.resolve_data">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">resolve_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">write_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.resolve_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.resolve_data" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">resolve_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">write_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.resolve_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.resolve_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform and prepare <code class="docutils literal notranslate"><span class="pre">write_item</span></code> from <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> for storage, ensuring idempotency and thread-safety.</p>
 <p>Lookup the object associated with <code class="docutils literal notranslate"><span class="pre">write_item</span></code> in <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and apply any
 transformation (such as serialization) prior to the storage layer consuming it.</p>
@@ -1645,7 +1650,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.set_up_planner">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_meta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_meta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize this planner to save <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.</p>
 <p>Implementations should save those values as they won’t be provided lated in the save process.</p>
 <p>This is called on all ranks.</p>
@@ -1657,20 +1662,20 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlan">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SavePlan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.distributed.checkpoint.planner.WriteItem" title="torch.distributed.checkpoint.planner.WriteItem"><span class="pre">torch.distributed.checkpoint.planner.WriteItem</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SavePlan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.distributed.checkpoint.planner.WriteItem" title="torch.distributed.checkpoint.planner.WriteItem"><span class="pre">torch.distributed.checkpoint.planner.WriteItem</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlan" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.planner.WriteItem">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.planner.</span></span><span class="sig-name descname"><span class="pre">WriteItem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#WriteItem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.planner.WriteItem" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.planner.</span></span><span class="sig-name descname"><span class="pre">WriteItem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#WriteItem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.planner.WriteItem" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataclass which holds information about what needs to be written to storage.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.planner.WriteItem.tensor_storage_size">
-<span class="sig-name descname"><span class="pre">tensor_storage_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#WriteItem.tensor_storage_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/planner.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.planner.WriteItem.tensor_storage_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">tensor_storage_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#WriteItem.tensor_storage_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.planner.WriteItem.tensor_storage_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculates the storage size of the underlying tensor, or None if this is not a tensor write.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1687,7 +1692,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>We provide a filesystem based storage layer:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.FileSystemReader">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">FileSystemReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extension_registry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/filesystem.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemReader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">FileSystemReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extension_registry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/filesystem.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemReader" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py property">
@@ -1700,7 +1705,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.FileSystemWriter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">FileSystemWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_file_per_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thread_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_thread_copy_ahead</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_staged_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extensions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/filesystem.py#L834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemWriter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">FileSystemWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_file_per_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thread_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_thread_copy_ahead</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_staged_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extensions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/filesystem.py#L834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemWriter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Basic implementation of StorageWriter using file IO.</p>
 <p>This implementation makes the following assumptions and simplifications:</p>
 <ul class="simple">
@@ -1713,7 +1718,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.FileSystemWriter.stage">
-<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemWriter.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/filesystem.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemWriter.stage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemWriter.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/filesystem.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemWriter.stage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Override of AsyncStager.stage</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1728,12 +1733,12 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 can handle all of torch.distributed constructs such as FSDP, DDP, ShardedTensor and DistributedTensor.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultSavePlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">DefaultSavePlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dedup_replicated_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dedup_save_to_lowest_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/default_planner.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">DefaultSavePlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dedup_replicated_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dedup_save_to_lowest_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultSavePlanner.lookup_object">
-<span class="sig-name descname"><span class="pre">lookup_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner.lookup_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/default_planner.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner.lookup_object" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">lookup_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner.lookup_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner.lookup_object" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension from the planner interface to make it easy to extend the default planner.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1744,7 +1749,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultSavePlanner.transform_object">
-<span class="sig-name descname"><span class="pre">transform_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">write_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner.transform_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/default_planner.py#L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner.transform_object" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">transform_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">write_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner.transform_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner.transform_object" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension from the planner interface to make it easy to extend the default planner.</p>
 <dl class="field-list simple">
 </dl>
@@ -1754,7 +1759,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultLoadPlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">DefaultLoadPlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_partial_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/default_planner.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">DefaultLoadPlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_partial_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner" title="Permalink to this definition">¶</a></dt>
 <dd><p>DefaultLoadPlanner that adds multiple features on top of LoadPlanner.</p>
 <p>In particular it adds the following:</p>
 <p>flatten_state_dict: Handle state_dict with nested dicts
@@ -1764,7 +1769,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultLoadPlanner.lookup_tensor">
-<span class="sig-name descname"><span class="pre">lookup_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner.lookup_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/default_planner.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner.lookup_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">lookup_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner.lookup_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner.lookup_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension from the planner interface to make it easy to extend the default planner.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1775,7 +1780,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultLoadPlanner.transform_tensor">
-<span class="sig-name descname"><span class="pre">transform_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner.transform_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/default_planner.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner.transform_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">transform_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner.transform_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner.transform_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension from the planner interface to make it easy to extend the default planner.</p>
 <dl class="field-list simple">
 </dl>
@@ -1789,7 +1794,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>Note that this feature is experimental, and API signatures might change in the future.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.get_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict.py#L1049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the model state_dict and optimizers state_dict.</p>
 <p><code class="docutils literal notranslate"><span class="pre">get_state_dict</span></code> can process any module that is parallelized by PyTorch
 FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
@@ -1856,7 +1861,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.get_model_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_model_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_model_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_model_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_model_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_model_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_model_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the model state_dict of <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p>
 <p>See <code class="docutils literal notranslate"><span class="pre">get_state_dict</span></code> for the detail usage.</p>
 <dl class="field-list simple">
@@ -1881,7 +1886,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.get_optimizer_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_optimizer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_optimizer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict.py#L1004"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_optimizer_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_optimizer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_optimizer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1004"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_optimizer_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the combined state_dict for optimizers.</p>
 <p>See <code class="docutils literal notranslate"><span class="pre">get_state_dict</span></code> for the detail usage.</p>
 <dl class="field-list simple">
@@ -1907,7 +1912,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.set_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict.py#L1249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the model state_dict and optimizers state_dict.</p>
 <p>The counterpart of <code class="docutils literal notranslate"><span class="pre">get_state_dict</span></code> to set the state_dict to the model and
 optimizers.  The given <code class="docutils literal notranslate"><span class="pre">model_state_dict</span></code> and <code class="docutils literal notranslate"><span class="pre">optim_state_dict</span></code> do not
@@ -1948,7 +1953,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.set_model_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_model_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_model_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict.py#L1171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_model_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_model_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_model_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_model_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the model state_dict.</p>
 <p>The counterpart of <code class="docutils literal notranslate"><span class="pre">get_model_state_dict</span></code> to set the state_dict to the
 model. See <code class="docutils literal notranslate"><span class="pre">set_state_dict</span></code> for the detail usage.</p>
@@ -1981,7 +1986,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.set_optimizer_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_optimizer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_optimizer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict.py#L1210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_optimizer_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_optimizer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_optimizer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_optimizer_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizers state_dict.</p>
 <p>The counterpart of <code class="docutils literal notranslate"><span class="pre">get_optimizer_state_dict</span></code> to set the state_dict to the
 optimizers. See <code class="docutils literal notranslate"><span class="pre">set_state_dict</span></code> for the detail usage.</p>
@@ -2008,7 +2013,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.StateDictOptions">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">StateDictOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_offload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_frozen_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_submodule_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">broadcast_from_rank0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_optimizer_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#StateDictOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/state_dict.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.StateDictOptions" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">StateDictOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_offload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_frozen_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_submodule_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">broadcast_from_rank0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_optimizer_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#StateDictOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.StateDictOptions" title="Permalink to this definition">¶</a></dt>
 <dd><p>This dataclass specifies how get_state_dict/set_state_dict will work.</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">full_state_dict</span></code>: if this is set to True, all the tensors in the
@@ -2048,7 +2053,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>For users which are used to using and sharing models in the <cite>torch.save</cite> format, the following methods are provided which provide offline utilities for converting betweeing formats.</p>
 <span class="target" id="module-torch.distributed.checkpoint.format_utils"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.dcp_to_torch_save">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">dcp_to_torch_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dcp_checkpoint_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_save_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#dcp_to_torch_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.dcp_to_torch_save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">dcp_to_torch_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dcp_checkpoint_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_save_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#dcp_to_torch_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.dcp_to_torch_save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a directory containing a DCP checkpoint, this function will convert it into a
 Torch save file.</p>
 <dl class="field-list simple">
@@ -2067,7 +2072,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.torch_save_to_dcp">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">torch_save_to_dcp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">torch_save_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dcp_checkpoint_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#torch_save_to_dcp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.torch_save_to_dcp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">torch_save_to_dcp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">torch_save_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dcp_checkpoint_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#torch_save_to_dcp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.torch_save_to_dcp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given the location of a torch save file, converts it into a DCP checkpoint.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2086,7 +2091,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>The following classes can also be utilized for online loading and resharding of models from the torch.save format.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">BroadcastingTorchSaveReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">BroadcastingTorchSaveReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader" title="Permalink to this definition">¶</a></dt>
 <dd><p>StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
 on the coordinator rank, and then broadcast and shard each tensor to all ranks.</p>
 <p>. N.B. Intended to be used with DynamicMetaLoadPlanner</p>
@@ -2107,7 +2112,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_global_plan">
-<span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2118,7 +2123,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_local_plan">
-<span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2129,7 +2134,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_data">
-<span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reads torch save data on the coordinator rank, and broadcast afterwards
 this incurrs a communication cost, but avoids having to load
 the entire checkpoint on each rank, hopefully preventing OOM issues</p>
@@ -2142,7 +2147,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_metadata">
-<span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_metadata" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_metadata" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extends the default StorageReader to support building the metadata file</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2153,7 +2158,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.reset">
-<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 </dl>
@@ -2161,7 +2166,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.set_up_storage_reader">
-<span class="sig-name descname"><span class="pre">set_up_storage_reader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.set_up_storage_reader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.set_up_storage_reader" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_up_storage_reader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.set_up_storage_reader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.set_up_storage_reader" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 </dl>
@@ -2169,7 +2174,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.validate_checkpoint_id">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2182,7 +2187,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">DynamicMetaLoadPlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_partial_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#DynamicMetaLoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">DynamicMetaLoadPlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_partial_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#DynamicMetaLoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
 avoiding the need to read metadata from disk. This is useful when reading formats which don’t have a
 metadata file, like Torch Save files.</p>
@@ -2204,7 +2209,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner.set_up_planner">
-<span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#DynamicMetaLoadPlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/checkpoint/format_utils.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#DynamicMetaLoadPlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Setups of the planner, extnding default behavior by creating the Metadata object from the state dict</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/distributed.elastic.html b/main/distributed.elastic.html
index b7a8b4b5ab4..9d395d88bfd 100644
--- a/main/distributed.elastic.html
+++ b/main/distributed.elastic.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/distributed.fsdp.fully_shard.html b/main/distributed.fsdp.fully_shard.html
index 5413c0a8f8c..9bf7dda5a52 100644
--- a/main/distributed.fsdp.fully_shard.html
+++ b/main/distributed.fsdp.fully_shard.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -660,7 +660,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 <p>The frontend API is <code class="docutils literal notranslate"><span class="pre">fully_shard</span></code> that can be called on a <code class="docutils literal notranslate"><span class="pre">module</span></code>:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.fully_shard">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">fully_shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reshard_after_forward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shard_placement_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mp_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">MixedPrecisionPolicy(param_dtype=None,</span> <span class="pre">reduce_dtype=None,</span> <span class="pre">output_dtype=None,</span> <span class="pre">cast_forward_inputs=True)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OffloadPolicy()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.fully_shard" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">fully_shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reshard_after_forward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shard_placement_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mp_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">MixedPrecisionPolicy(param_dtype=None,</span> <span class="pre">reduce_dtype=None,</span> <span class="pre">output_dtype=None,</span> <span class="pre">cast_forward_inputs=True)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OffloadPolicy()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.fully_shard" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply fully sharded data parallelism (FSDP) to <code class="docutils literal notranslate"><span class="pre">module</span></code>, where FSDP
 shards module parameters, gradients, and optimizer states across data
 parallel workers to save memory at the cost of communication.</p>
@@ -760,7 +760,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FSDPModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.reshard">
-<span class="sig-name descname"><span class="pre">reshard</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.reshard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.reshard" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reshard</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.reshard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.reshard" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reshards the module’s parameters, freeing the unsharded parameters if
 they are allocated and registering the sharded parameters to the
 module. This method is <em>not</em> recursive.</p>
@@ -770,7 +770,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_is_last_backward">
-<span class="sig-name descname"><span class="pre">set_is_last_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_last_backward</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_is_last_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_is_last_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_is_last_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_last_backward</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_is_last_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_is_last_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether the next backward is the last one. On the last backward,
 FSDP waits on pending gradient reduction and clears internal data
 data structures for backward prefetching. This can be useful for
@@ -781,7 +781,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_modules_to_backward_prefetch">
-<span class="sig-name descname"><span class="pre">set_modules_to_backward_prefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_modules_to_backward_prefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_modules_to_backward_prefetch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_modules_to_backward_prefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_modules_to_backward_prefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_modules_to_backward_prefetch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the FSDP modules for which this FSDP module should explicitly
 prefetch all-gathers in backward. This overrides the default backward
 pretching implementation that prefetches the next FSDP module based on
@@ -799,7 +799,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_modules_to_forward_prefetch">
-<span class="sig-name descname"><span class="pre">set_modules_to_forward_prefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_modules_to_forward_prefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_modules_to_forward_prefetch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_modules_to_forward_prefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_modules_to_forward_prefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_modules_to_forward_prefetch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the FSDP modules for which this FSDP module should explicitly
 prefetch all-gathers in forward. The prefetching runs after this
 module’s all-gather copy-out.</p>
@@ -817,7 +817,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_post_optim_event">
-<span class="sig-name descname"><span class="pre">set_post_optim_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_post_optim_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_post_optim_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_post_optim_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_post_optim_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_post_optim_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets a post-optimizer-step event for the root FSDP module to wait the
 all-gather streams on.</p>
 <p>By default, the root FSDP module waits the all-gather streams on the
@@ -837,7 +837,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_reduce_scatter_divide_factor">
-<span class="sig-name descname"><span class="pre">set_reduce_scatter_divide_factor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_reduce_scatter_divide_factor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_reduce_scatter_divide_factor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_reduce_scatter_divide_factor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_reduce_scatter_divide_factor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_reduce_scatter_divide_factor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets a custom divide factor for the reduce-scatter. This becomes a
 custom reduce op using NCCL’s PreMulSum, which allows multiplying by
 the factor before reduction.</p>
@@ -850,7 +850,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_requires_all_reduce">
-<span class="sig-name descname"><span class="pre">set_requires_all_reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_all_reduce</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_requires_all_reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_requires_all_reduce" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_requires_all_reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_all_reduce</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_requires_all_reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_requires_all_reduce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets if the module should all-reduce gradients. This can be used to
 implement gradient accumulation with only reduce-scatter but not
 all-reduce for HSDP.</p>
@@ -860,7 +860,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_requires_gradient_sync">
-<span class="sig-name descname"><span class="pre">set_requires_gradient_sync</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_gradient_sync</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_requires_gradient_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_requires_gradient_sync" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_requires_gradient_sync</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_gradient_sync</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_requires_gradient_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_requires_gradient_sync" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets if the module should sync gradients. This can be used to implement
 gradient accumulation <em>without communication</em>. For HSDP, this controls
 both reduce-scatter and all-reduce together.</p>
@@ -878,7 +878,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_reshard_after_backward">
-<span class="sig-name descname"><span class="pre">set_reshard_after_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reshard_after_backward</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_reshard_after_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_reshard_after_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_reshard_after_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reshard_after_backward</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_reshard_after_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_reshard_after_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets if the module should reshard parameters after backward. This can
 be used during gradient accumulation to trade off higher memory for
 reduced communication since the unsharded parameters do not need to be
@@ -897,7 +897,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_unshard_in_backward">
-<span class="sig-name descname"><span class="pre">set_unshard_in_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">unshard_in_backward</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_unshard_in_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L398"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_unshard_in_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_unshard_in_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">unshard_in_backward</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_unshard_in_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L398"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_unshard_in_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether the FSDP module’s parameters need to be unsharded in
 backward. This can be used in expert cases when the user knows that all
 parameters in this FSDP module’s parameter group are not needed for
@@ -908,7 +908,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.unshard">
-<span class="sig-name descname"><span class="pre">unshard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.unshard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.unshard" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">unshard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.unshard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.unshard" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unshards the module’s parameters by allocating memory and all-gathering
 the parameters. This method is <em>not</em> recursive. The unshard follows the
 <a class="reference internal" href="#torch.distributed.fsdp.MixedPrecisionPolicy" title="torch.distributed.fsdp.MixedPrecisionPolicy"><code class="xref py py-class docutils literal notranslate"><span class="pre">MixedPrecisionPolicy</span></code></a>, so it will all-gather following
@@ -941,7 +941,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 <dd><p>A handle to wait on a <a class="reference internal" href="#torch.distributed.fsdp.FSDPModule.unshard" title="torch.distributed.fsdp.FSDPModule.unshard"><code class="xref py py-meth docutils literal notranslate"><span class="pre">FSDPModule.unshard()</span></code></a> op.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.UnshardHandle.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#UnshardHandle.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.UnshardHandle.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#UnshardHandle.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.UnshardHandle.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits on the unshard op. This ensures that the current stream can use
 the unsharded parameters, which are now registered to the module.</p>
 <dl class="field-list simple">
@@ -952,7 +952,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.register_fsdp_forward_method">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">register_fsdp_forward_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L472"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.register_fsdp_forward_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">register_fsdp_forward_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L472"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.register_fsdp_forward_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a method on <code class="docutils literal notranslate"><span class="pre">module</span></code> to be considered a forward method for
 FSDP.</p>
 <p>FSDP all-gathers parameters pre-forward and optionally frees parameters
diff --git a/main/distributed.html b/main/distributed.html
index c87e2d7efc9..26763d41ef9 100644
--- a/main/distributed.html
+++ b/main/distributed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -860,7 +860,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return <code class="docutils literal notranslate"><span class="pre">True</span></code> if the distributed package is available.</p>
 <p>Otherwise,
 <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> does not expose any other APIs. Currently,
@@ -877,7 +877,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.init_process_group">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">init_process_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pg_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#init_process_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1534"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.init_process_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">init_process_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pg_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#init_process_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1534"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.init_process_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize the default distributed process group.</p>
 <p>This will also initialize the distributed package.</p>
 <dl class="simple">
@@ -968,7 +968,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.init_device_mesh">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.device_mesh.</span></span><span class="sig-name descname"><span class="pre">init_device_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_shape</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#init_device_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/device_mesh.py#L940"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.init_device_mesh" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.device_mesh.</span></span><span class="sig-name descname"><span class="pre">init_device_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_shape</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#init_device_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L940"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.init_device_mesh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initializes a <cite>DeviceMesh</cite> based on <cite>device_type</cite>, <cite>mesh_shape</cite>, and <cite>mesh_dim_names</cite> parameters.</p>
 <p>This creates a DeviceMesh with an n-dimensional array layout, where <cite>n</cite> is the length of <cite>mesh_shape</cite>.
 If <cite>mesh_dim_names</cite> is provided, each dimension is labeled as <cite>mesh_dim_names[i]</cite>.</p>
@@ -1015,7 +1015,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_initialized">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_initialized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_initialized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the default process group has been initialized.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1026,7 +1026,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_mpi_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_mpi_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_mpi_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_mpi_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_mpi_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_mpi_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_mpi_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the MPI backend is available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1037,7 +1037,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_nccl_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_nccl_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_nccl_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_nccl_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_nccl_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_nccl_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_nccl_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the NCCL backend is available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1048,7 +1048,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_gloo_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_gloo_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_gloo_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_gloo_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_gloo_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_gloo_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_gloo_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the Gloo backend is available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1059,7 +1059,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.distributed_c10d.is_xccl_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.distributed_c10d.</span></span><span class="sig-name descname"><span class="pre">is_xccl_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_xccl_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.distributed_c10d.is_xccl_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.distributed_c10d.</span></span><span class="sig-name descname"><span class="pre">is_xccl_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_xccl_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.distributed_c10d.is_xccl_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the XCCL backend is available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1070,7 +1070,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_torchelastic_launched">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_torchelastic_launched</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_torchelastic_launched"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_torchelastic_launched" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_torchelastic_launched</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_torchelastic_launched"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_torchelastic_launched" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check whether this process was launched with <code class="docutils literal notranslate"><span class="pre">torch.distributed.elastic</span></code> (aka torchelastic).</p>
 <p>The existence of <code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_RUN_ID</span></code> environment
 variable is used as a proxy to determine whether the current process
@@ -1165,7 +1165,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 check whether the process group has already been initialized use <a class="reference internal" href="#torch.distributed.is_initialized" title="torch.distributed.is_initialized"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.distributed.is_initialized()</span></code></a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.Backend">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">Backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.Backend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">Backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.Backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>An enum-like class for backends.</p>
 <p>Available backends: GLOO, NCCL, UCC, MPI, XCCL, and other registered backends.</p>
 <p>The values of this class are lowercase strings, e.g., <code class="docutils literal notranslate"><span class="pre">&quot;gloo&quot;</span></code>. They can
@@ -1184,7 +1184,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.Backend.register_backend">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">register_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extended_api</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend.register_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.Backend.register_backend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">register_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extended_api</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend.register_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.Backend.register_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a new backend with the given name and instantiating function.</p>
 <p>This class method is used by 3rd party <code class="docutils literal notranslate"><span class="pre">ProcessGroup</span></code> extension to
 register new backends.</p>
@@ -1217,7 +1217,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the backend of the given process group.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1236,7 +1236,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_rank">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the rank of the current process in the provided <code class="docutils literal notranslate"><span class="pre">group</span></code>, default otherwise.</p>
 <p>Rank is a unique identifier assigned to each process within a distributed
 process group. They are always consecutive integers ranging from 0 to
@@ -1258,7 +1258,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_world_size">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_world_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_world_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_world_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_world_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_world_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_world_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of processes in the current process group.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1314,7 +1314,7 @@ <h2>Groups<a class="headerlink" href="#groups" title="Permalink to this heading"
 (collectives are distributed functions to exchange information in certain well-known programming patterns).</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.new_group">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">new_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ranks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pg_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_synchronization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_desc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#new_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L4901"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.new_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">new_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ranks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pg_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_synchronization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_desc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#new_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4901"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.new_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new distributed group.</p>
 <p>This function requires that all processes in the main group (i.e. all
 processes that are part of the distributed job) enter this function, even
@@ -1382,7 +1382,7 @@ <h2>Groups<a class="headerlink" href="#groups" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_group_rank">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_group_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_rank</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_group_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1014"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_group_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_group_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_rank</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_group_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1014"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_group_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Translate a global rank into a group rank.</p>
 <p><code class="docutils literal notranslate"><span class="pre">global_rank</span></code> must be part of <code class="docutils literal notranslate"><span class="pre">group</span></code> otherwise this raises RuntimeError.</p>
 <dl class="field-list simple">
@@ -1404,7 +1404,7 @@ <h2>Groups<a class="headerlink" href="#groups" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_global_rank">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_global_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_rank</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_global_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1042"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_global_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_global_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_rank</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_global_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1042"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_global_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Translate a group rank into a global rank.</p>
 <p><code class="docutils literal notranslate"><span class="pre">group_rank</span></code> must be part of <cite>group</cite> otherwise this raises RuntimeError.</p>
 <dl class="field-list simple">
@@ -1426,7 +1426,7 @@ <h2>Groups<a class="headerlink" href="#groups" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_process_group_ranks">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_process_group_ranks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_process_group_ranks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L1080"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_process_group_ranks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_process_group_ranks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_process_group_ranks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1080"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_process_group_ranks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get all ranks associated with <code class="docutils literal notranslate"><span class="pre">group</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1451,7 +1451,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 used to create new DeviceMesh, with a mesh shape describing the device topology.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.device_mesh.</span></span><span class="sig-name descname"><span class="pre">DeviceMesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_init_backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/device_mesh.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.device_mesh.</span></span><span class="sig-name descname"><span class="pre">DeviceMesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_init_backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh" title="Permalink to this definition">¶</a></dt>
 <dd><p>DeviceMesh represents a mesh of devices, where layout of devices could be
 represented as a n-d dimension array, and each value of the n-d dimensional
 array is the global id of the default process group ranks.</p>
@@ -1497,7 +1497,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.from_group">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.from_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/device_mesh.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.from_group" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.from_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.from_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a <a class="reference internal" href="#torch.distributed.device_mesh.DeviceMesh" title="torch.distributed.device_mesh.DeviceMesh"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code></a> with <code class="docutils literal notranslate"><span class="pre">device_type</span></code> from an
 existing <code class="xref py py-class docutils literal notranslate"><span class="pre">ProcessGroup</span></code>.</p>
 <p>The constructed device mesh has number of dimensions equal to the
@@ -1512,7 +1512,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_all_groups">
-<span class="sig-name descname"><span class="pre">get_all_groups</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_all_groups"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/device_mesh.py#L778"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_all_groups" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_all_groups</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_all_groups"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L778"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_all_groups" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of ProcessGroups for all mesh dimensions.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1526,7 +1526,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_coordinate">
-<span class="sig-name descname"><span class="pre">get_coordinate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_coordinate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/device_mesh.py#L913"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_coordinate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_coordinate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_coordinate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L913"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_coordinate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the relative indices of this rank relative to all
 dimensions of the mesh. If this rank is not part of the mesh, return None.</p>
 <dl class="field-list simple">
@@ -1538,7 +1538,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_group">
-<span class="sig-name descname"><span class="pre">get_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/device_mesh.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the single ProcessGroup specified by mesh_dim, or, if mesh_dim is not specified and the
 DeviceMesh is 1-dimensional, returns the only ProcessGroup in the mesh.</p>
 <dl class="field-list simple">
@@ -1559,7 +1559,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_local_rank">
-<span class="sig-name descname"><span class="pre">get_local_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_local_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/device_mesh.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_local_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_local_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_local_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_local_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the local rank of the given mesh_dim of the DeviceMesh.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1597,7 +1597,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_rank">
-<span class="sig-name descname"><span class="pre">get_rank</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/device_mesh.py#L865"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_rank</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L865"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current global rank.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1613,7 +1613,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-communication" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.send">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">send</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#send"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.send" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">send</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#send"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.send" title="Permalink to this definition">¶</a></dt>
 <dd><p>Send a tensor synchronously.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1636,7 +1636,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.recv">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">recv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#recv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2454"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.recv" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">recv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#recv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2454"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.recv" title="Permalink to this definition">¶</a></dt>
 <dd><p>Receives a tensor synchronously.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1674,7 +1674,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 </ul>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.isend">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">isend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#isend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.isend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">isend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#isend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.isend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Send a tensor asynchronously.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1709,7 +1709,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.irecv">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">irecv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#irecv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.irecv" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">irecv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#irecv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.irecv" title="Permalink to this definition">¶</a></dt>
 <dd><p>Receives a tensor asynchronously.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1740,7 +1740,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.send_object_list">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">send_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#send_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L3179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.send_object_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">send_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#send_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.send_object_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sends picklable objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> synchronously.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.send" title="torch.distributed.send"><code class="xref py py-func docutils literal notranslate"><span class="pre">send()</span></code></a>, but Python objects can be passed in.
 Note that all objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> must be picklable in order to be
@@ -1809,7 +1809,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.recv_object_list">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">recv_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#recv_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L3278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.recv_object_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">recv_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#recv_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.recv_object_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Receives picklable objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> synchronously.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.recv" title="torch.distributed.recv"><code class="xref py py-func docutils literal notranslate"><span class="pre">recv()</span></code></a>, but can receive Python objects.</p>
 <dl class="field-list simple">
@@ -1876,7 +1876,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.batch_isend_irecv">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">batch_isend_irecv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p2p_op_list</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#batch_isend_irecv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.batch_isend_irecv" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">batch_isend_irecv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p2p_op_list</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#batch_isend_irecv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.batch_isend_irecv" title="Permalink to this definition">¶</a></dt>
 <dd><p>Send or Receive a batch of tensors asynchronously and return a list of requests.</p>
 <p>Process each of the operations in <code class="docutils literal notranslate"><span class="pre">p2p_op_list</span></code> and return the corresponding
 requests. NCCL, Gloo, and UCC backend are currently supported.</p>
@@ -1923,7 +1923,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.P2POp">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">P2POp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_peer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#P2POp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.P2POp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">P2POp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_peer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#P2POp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.P2POp" title="Permalink to this definition">¶</a></dt>
 <dd><p>A class to build point-to-point operations for <code class="docutils literal notranslate"><span class="pre">batch_isend_irecv</span></code>.</p>
 <p>This class builds the type of P2P operation, communication buffer, peer rank,
 Process Group, and tag. Instances of this class will be passed to
@@ -1998,7 +1998,7 @@ <h2>Synchronous and asynchronous collective operations<a class="headerlink" href
 <h2>Collective functions<a class="headerlink" href="#collective-functions" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.broadcast">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#broadcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.broadcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#broadcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.broadcast" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcasts the tensor to the whole group.</p>
 <p><code class="docutils literal notranslate"><span class="pre">tensor</span></code> must have the same number of elements in all processes
 participating in the collective.</p>
@@ -2024,7 +2024,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.broadcast_object_list">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">broadcast_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#broadcast_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L3380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.broadcast_object_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">broadcast_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#broadcast_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.broadcast_object_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcasts picklable objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> to the whole group.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.broadcast" title="torch.distributed.broadcast"><code class="xref py py-func docutils literal notranslate"><span class="pre">broadcast()</span></code></a>, but Python objects can be passed in.
 Note that all objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> must be picklable in order to be
@@ -2100,7 +2100,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_reduce">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_reduce" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_reduce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduces the tensor data across all machines in a way that all get the final result.</p>
 <p>After the call <code class="docutils literal notranslate"><span class="pre">tensor</span></code> is going to be bitwise identical in all processes.</p>
 <p>Complex tensors are supported.</p>
@@ -2152,7 +2152,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.reduce">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduces the tensor data across all machines.</p>
 <p>Only the process with rank <code class="docutils literal notranslate"><span class="pre">dst</span></code> is going to receive the final result.</p>
 <dl class="field-list simple">
@@ -2180,7 +2180,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_gather">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L3636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers tensors from the whole group in a list.</p>
 <p>Complex and uneven sized tensors are supported.</p>
 <dl class="field-list simple">
@@ -2238,7 +2238,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_gather_into_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather_into_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather_into_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L3711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather_into_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather_into_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather_into_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather_into_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gather tensors from all ranks and put them in a single output tensor.</p>
 <p>This function requires all tensors to be the same size on each process.</p>
 <dl class="field-list simple">
@@ -2297,7 +2297,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_gather_object">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L2963"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather_object" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2963"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather_object" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers picklable objects from the whole group into a list.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.all_gather" title="torch.distributed.all_gather"><code class="xref py py-func docutils literal notranslate"><span class="pre">all_gather()</span></code></a>, but Python objects can be passed in.
 Note that the object must be picklable in order to be gathered.</p>
@@ -2363,7 +2363,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.gather">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gather_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L3933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.gather" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gather_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.gather" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers a list of tensors in a single process.</p>
 <p>This function requires all tensors to be the same size on each process.</p>
 <dl class="field-list simple">
@@ -2412,7 +2412,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.gather_object">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">gather_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object_gather_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#gather_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L3054"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.gather_object" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">gather_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object_gather_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#gather_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3054"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.gather_object" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers picklable objects from the whole group in a single process.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.gather" title="torch.distributed.gather"><code class="xref py py-func docutils literal notranslate"><span class="pre">gather()</span></code></a>, but Python objects can be passed in. Note that the
 object must be picklable in order to be gathered.</p>
@@ -2486,7 +2486,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.scatter">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L4013"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.scatter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4013"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.scatter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatters a list of tensors to all processes in a group.</p>
 <p>Each process will receive exactly one tensor and store its data in the
 <code class="docutils literal notranslate"><span class="pre">tensor</span></code> argument.</p>
@@ -2541,7 +2541,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.scatter_object_list">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">scatter_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scatter_object_output_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_object_input_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#scatter_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L3506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.scatter_object_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">scatter_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scatter_object_output_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_object_input_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#scatter_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.scatter_object_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatters picklable objects in <code class="docutils literal notranslate"><span class="pre">scatter_object_input_list</span></code> to the whole group.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.scatter" title="torch.distributed.scatter"><code class="xref py py-func docutils literal notranslate"><span class="pre">scatter()</span></code></a>, but Python objects can be passed in. On
 each rank, the scattered object will be stored as the first element of
@@ -2609,7 +2609,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.reduce_scatter">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce_scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce_scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L4117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce_scatter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce_scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce_scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce_scatter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduces, then scatters a list of tensors to all processes in a group.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2633,7 +2633,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.reduce_scatter_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce_scatter_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce_scatter_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L4156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce_scatter_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce_scatter_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce_scatter_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce_scatter_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduces, then scatters a tensor to all ranks in a group.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2693,7 +2693,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_to_all_single">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_to_all_single</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_split_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_split_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_to_all_single"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L4270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_to_all_single" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_to_all_single</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_split_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_split_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_to_all_single"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_to_all_single" title="Permalink to this definition">¶</a></dt>
 <dd><p>Split input tensor and then scatter the split list to all processes in a group.</p>
 <p>Later the received tensors are concatenated from all the processes in the group
 and returned as a single output tensor.</p>
@@ -2791,7 +2791,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_to_all">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_to_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_to_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L4397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_to_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_to_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_to_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_to_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.</p>
 <p>Complex tensors are supported.</p>
 <dl class="field-list simple">
@@ -2889,7 +2889,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.barrier">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">barrier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L4513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.barrier" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">barrier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.barrier" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize all processes.</p>
 <p>This collective blocks processes until the whole group enters this function,
 if async_op is False, or if async work handle is called on wait().</p>
@@ -2915,7 +2915,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.monitored_barrier">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">monitored_barrier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_all_ranks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#monitored_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/distributed_c10d.py#L4558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.monitored_barrier" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">monitored_barrier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_all_ranks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#monitored_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.monitored_barrier" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize processes similar to <code class="docutils literal notranslate"><span class="pre">torch.distributed.barrier</span></code>, but consider a configurable timeout.</p>
 <p>It is able to report ranks that did not pass this barrier within the provided timeout.
 Specifically, for non-zero ranks, will block until a send/recv is processed from rank 0.
@@ -4161,7 +4161,7 @@ <h2>Logging<a class="headerlink" href="#logging" title="Permalink to this headin
 <p>If you are running single node training, it may be convenient to interactively breakpoint your script.  We offer a way to conveniently breakpoint a single rank:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.breakpoint">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">breakpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#breakpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/__init__.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.breakpoint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">breakpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#breakpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/__init__.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.breakpoint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set a breakpoint, but only on a single rank.  All other ranks will wait for you to be
 done with the breakpoint before continuing.</p>
 <dl class="field-list simple">
diff --git a/main/distributed.optim.html b/main/distributed.optim.html
index a658dba8802..8e4cddebd4b 100644
--- a/main/distributed.optim.html
+++ b/main/distributed.optim.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -619,7 +619,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 apply the gradients on each worker.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.optim.DistributedOptimizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">DistributedOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params_rref</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/optimizer.html#DistributedOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/optimizer.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">DistributedOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params_rref</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/optimizer.html#DistributedOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/optimizer.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>DistributedOptimizer takes remote references to parameters scattered
 across workers and applies the given optimizer locally for each parameter.</p>
 <p>This class uses <a class="reference internal" href="rpc.html#torch.distributed.autograd.get_gradients" title="torch.distributed.autograd.get_gradients"><code class="xref py py-meth docutils literal notranslate"><span class="pre">get_gradients()</span></code></a> in order
@@ -679,7 +679,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.DistributedOptimizer.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">context_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/optimizer.html#DistributedOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/optimizer.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">context_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/optimizer.html#DistributedOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/optimizer.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs a single optimization step.</p>
 <p>This will call <a class="reference internal" href="generated/torch.optim.Optimizer.step.html#torch.optim.Optimizer.step" title="torch.optim.Optimizer.step"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.optim.Optimizer.step()</span></code></a> on each worker
 containing parameters to be optimized, and will block until all workers
@@ -698,7 +698,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">PostLocalSGDOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">averager</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/post_localSGD_optimizer.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">PostLocalSGDOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">averager</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/post_localSGD_optimizer.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wraps an arbitrary <a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> and runs <a class="reference external" href="https://arxiv.org/abs/1808.07217">post-local SGD</a>,
 This optimizer runs local optimizer at every step.
 After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.</p>
@@ -750,7 +750,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/post_localSGD_optimizer.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/post_localSGD_optimizer.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the same as <a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> <a class="reference internal" href="#torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict" title="torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a>,
 but also restores model averager’s step value to the one
 saved in the provided <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.</p>
@@ -760,7 +760,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/post_localSGD_optimizer.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/post_localSGD_optimizer.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the same as <a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> <a class="reference internal" href="#torch.distributed.optim.PostLocalSGDOptimizer.state_dict" title="torch.distributed.optim.PostLocalSGDOptimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a>,
 but adds an extra entry to record model averager’s step to the checkpoint
 to ensure reload does not cause unnecessary warm up again.</p>
@@ -768,7 +768,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/post_localSGD_optimizer.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/post_localSGD_optimizer.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs a single optimization step (parameter update).</p>
 </dd></dl>
 
@@ -776,7 +776,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">ZeroRedundancyOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters_as_bucket_view</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap_with_ddp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/zero_redundancy_optimizer.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">ZeroRedundancyOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters_as_bucket_view</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap_with_ddp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap an arbitrary <a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">optim.Optimizer</span></code></a> and shards its states across ranks in the group.</p>
 <p>The sharing is done as described by <a class="reference external" href="https://arxiv.org/abs/1910.02054">ZeRO</a>.</p>
 <p>The local optimizer instance in each rank is only
@@ -866,7 +866,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.add_param_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/zero_redundancy_optimizer.py#L460"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.add_param_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L460"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a parameter group to the <code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code> ‘s <code class="docutils literal notranslate"><span class="pre">param_groups</span></code>.</p>
 <p>This can be useful when fine tuning a pre-trained network, as frozen
 layers can be made trainable and added to the <code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code> as
@@ -889,7 +889,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict">
-<span class="sig-name descname"><span class="pre">consolidate_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.consolidate_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/zero_redundancy_optimizer.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">consolidate_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.consolidate_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Consolidate a list of <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> s (one per rank) on the target rank.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -917,7 +917,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.join_hook">
-<span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/zero_redundancy_optimizer.py#L1121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.join_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L1121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.join_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the ZeRO join hook.</p>
 <p>It enables training on uneven inputs by
 shadowing the collective communications in the optimizer step.</p>
@@ -942,7 +942,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/zero_redundancy_optimizer.py#L1151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L1151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the state pertaining to the given rank from the input <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>, updating the local optimizer as needed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -961,7 +961,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/zero_redundancy_optimizer.py#L1189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L1189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the last global optimizer state known to this rank.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Raises</dt>
@@ -980,7 +980,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/optim/zero_redundancy_optimizer.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimizer step and syncs parameters across all ranks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/distributed.pipelining.html b/main/distributed.pipelining.html
index 72a97540deb..08b2bf5fcce 100644
--- a/main/distributed.pipelining.html
+++ b/main/distributed.pipelining.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -976,7 +976,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 <p>The following set of APIs transform your model into a pipeline representation.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.SplitPoint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">SplitPoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#SplitPoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/_IR.py#L1145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.SplitPoint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">SplitPoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#SplitPoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/_IR.py#L1145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.SplitPoint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enum representing the points at which a split can occur in the execution of a submodule.
 :ivar BEGINNING: Represents adding a split point <em>before</em> the execution of a certain submodule in the <cite>forward</cite> function.
 :ivar END: Represents adding a split point <em>after</em> the execution of a certain submodule in the <cite>forward</cite> function.</p>
@@ -984,7 +984,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.pipeline">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mb_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mb_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#pipeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/_IR.py#L1200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.pipeline" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mb_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mb_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#pipeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/_IR.py#L1200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.pipeline" title="Permalink to this definition">¶</a></dt>
 <dd><p>Split a module based on a specification.</p>
 <p>See <cite>Pipe</cite> for more details.</p>
 <dl class="field-list simple">
@@ -1005,14 +1005,14 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.Pipe">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">Pipe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_gm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_loss_and_backward</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#Pipe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/_IR.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.Pipe" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">Pipe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_gm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_loss_and_backward</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#Pipe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/_IR.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.Pipe" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.pipe_split">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">pipe_split</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#pipe_split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/_IR.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.pipe_split" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">pipe_split</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#pipe_split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/_IR.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.pipe_split" title="Permalink to this definition">¶</a></dt>
 <dd><p>pipe_split is a special operator that is used to mark the boundary between
 stages in a module. It is used to split the module into stages. It is a
 no-op if your annotated module is run eagerly.</p>
@@ -1033,7 +1033,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 <span id="microbatch-utilities"></span><h3>Microbatch Utilities<a class="headerlink" href="#module-torch.distributed.pipelining.microbatch" title="Permalink to this heading">¶</a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.microbatch.TensorChunkSpec">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">TensorChunkSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#TensorChunkSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/microbatch.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.TensorChunkSpec" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">TensorChunkSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#TensorChunkSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/microbatch.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.TensorChunkSpec" title="Permalink to this definition">¶</a></dt>
 <dd><p>Class used to specify chunking of inputs</p>
 <dl class="field-list simple">
 </dl>
@@ -1041,7 +1041,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.microbatch.split_args_kwargs_into_chunks">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">split_args_kwargs_into_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#split_args_kwargs_into_chunks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/microbatch.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.split_args_kwargs_into_chunks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">split_args_kwargs_into_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#split_args_kwargs_into_chunks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/microbatch.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.split_args_kwargs_into_chunks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a sequence of args and kwargs, split them into a number of chunks
 according to  their respective chunking specs.</p>
 <dl class="field-list simple">
@@ -1066,7 +1066,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.microbatch.merge_chunks">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">merge_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#merge_chunks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/microbatch.py#L349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.merge_chunks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">merge_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#merge_chunks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/microbatch.py#L349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.merge_chunks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a list of chunks, merge them into a single value according to
 the chunk spec.</p>
 <dl class="field-list simple">
@@ -1090,7 +1090,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 <span id="pipeline-stages"></span><h3>Pipeline Stages<a class="headerlink" href="#module-torch.distributed.pipelining.stage" title="Permalink to this heading">¶</a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.stage.PipelineStage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.stage.</span></span><span class="sig-name descname"><span class="pre">PipelineStage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">submodule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dw_builder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/stage.html#PipelineStage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/stage.py#L1244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.stage.PipelineStage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.stage.</span></span><span class="sig-name descname"><span class="pre">PipelineStage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">submodule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dw_builder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/stage.html#PipelineStage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/stage.py#L1244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.stage.PipelineStage" title="Permalink to this definition">¶</a></dt>
 <dd><p>A class representing a pipeline stage in a pipeline parallelism setup.</p>
 <p>PipelineStage assumes sequential partitioning of the model, i.e. the model is split into chunks where outputs from
 one chunk feed into inputs of the next chunk, with no skip connections.</p>
@@ -1115,7 +1115,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.stage.build_stage">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.stage.</span></span><span class="sig-name descname"><span class="pre">build_stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipe_info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/stage.html#build_stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/stage.py#L1214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.stage.build_stage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.stage.</span></span><span class="sig-name descname"><span class="pre">build_stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipe_info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/stage.html#build_stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/stage.py#L1214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.stage.build_stage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a pipeline stage given a stage_module to be wrapped by this stage
 and pipeline information.</p>
 <dl class="field-list simple">
@@ -1142,7 +1142,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 <span id="pipeline-schedules"></span><h3>Pipeline Schedules<a class="headerlink" href="#module-torch.distributed.pipelining.schedules" title="Permalink to this heading">¶</a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleGPipe">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleGPipe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleGPipe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L571"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleGPipe" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleGPipe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleGPipe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L571"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleGPipe" title="Permalink to this definition">¶</a></dt>
 <dd><p>The GPipe schedule.
 Will go through all the microbatches in a fill-drain manner.</p>
 <dl class="field-list simple">
@@ -1151,7 +1151,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.Schedule1F1B">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">Schedule1F1B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#Schedule1F1B"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L662"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.Schedule1F1B" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">Schedule1F1B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#Schedule1F1B"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L662"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.Schedule1F1B" title="Permalink to this definition">¶</a></dt>
 <dd><p>The 1F1B schedule.
 Will perform one forward and one backward on the microbatches in steady state.</p>
 <dl class="field-list simple">
@@ -1160,7 +1160,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleInterleaved1F1B">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleInterleaved1F1B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleInterleaved1F1B"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L1933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleInterleaved1F1B" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleInterleaved1F1B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleInterleaved1F1B"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L1933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleInterleaved1F1B" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Interleaved 1F1B schedule.
 See <a class="reference external" href="https://arxiv.org/pdf/2104.04473">https://arxiv.org/pdf/2104.04473</a> for details.
 Will perform one forward and one backward on the microbatches in steady
@@ -1181,7 +1181,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleLoopedBFS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleLoopedBFS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleLoopedBFS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L1732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleLoopedBFS" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleLoopedBFS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleLoopedBFS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L1732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleLoopedBFS" title="Permalink to this definition">¶</a></dt>
 <dd><p>Breadth-First Pipeline Parallelism.
 See <a class="reference external" href="https://arxiv.org/abs/2211.05953">https://arxiv.org/abs/2211.05953</a> for details.
 Simliar to Interleaved 1F1B, Looped BFS supports multiple stages per rank.
@@ -1194,7 +1194,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleInterleavedZeroBubble">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleInterleavedZeroBubble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleInterleavedZeroBubble"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L2049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleInterleavedZeroBubble" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleInterleavedZeroBubble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleInterleavedZeroBubble"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L2049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleInterleavedZeroBubble" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Interleaved Zero Bubble schedule.
 See <a class="reference external" href="https://arxiv.org/pdf/2401.10241">https://arxiv.org/pdf/2401.10241</a> for details.
 Will perform one forward and one backward on inputs for the microbatches in steady
@@ -1207,7 +1207,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleZBVZeroBubble">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleZBVZeroBubble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index_to_group_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleZBVZeroBubble"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L2245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleZBVZeroBubble" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleZBVZeroBubble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index_to_group_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleZBVZeroBubble"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L2245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleZBVZeroBubble" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Zero Bubble schedule (ZBV variant).
 See <a class="reference external" href="https://arxiv.org/pdf/2401.10241">https://arxiv.org/pdf/2401.10241</a> Section 6 for details.</p>
 <p>This schedules requires exactly two stages per rank.</p>
@@ -1223,7 +1223,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.PipelineScheduleSingle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">PipelineScheduleSingle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleSingle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleSingle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">PipelineScheduleSingle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleSingle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleSingle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for single-stage schedules.
 Implements the <cite>step</cite> method.
 Derived classes should implement <cite>_step_microbatches</cite>.</p>
@@ -1234,7 +1234,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.PipelineScheduleSingle.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">losses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleSingle.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleSingle.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">losses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleSingle.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleSingle.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run one iteration of the pipeline schedule with <em>whole-batch</em> input.
 Will chunk the input into microbatches automatically, and go through the
 microbatches according to the schedule implementation.</p>
@@ -1250,7 +1250,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.PipelineScheduleMulti">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">PipelineScheduleMulti</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index_to_group_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_full_backward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleMulti"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L1089"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleMulti" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">PipelineScheduleMulti</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index_to_group_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_full_backward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleMulti"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L1089"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleMulti" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for multi-stage schedules.
 Implements the <cite>step</cite> method.</p>
 <p>Gradients are scaled by num_microbatches depending on the <cite>scale_grads</cite> argument, defaulting to True.  This setting
@@ -1260,7 +1260,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.PipelineScheduleMulti.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">losses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleMulti.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/pipelining/schedules.py#L1192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleMulti.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">losses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleMulti.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L1192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleMulti.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run one iteration of the pipeline schedule with <em>whole-batch</em> input.
 Will chunk the input into microbatches automatically, and go through the
 microbatches according to the schedule implementation.</p>
diff --git a/main/distributed.tensor.html b/main/distributed.tensor.html
index 47e0ad5bd44..ffecb963977 100644
--- a/main/distributed.tensor.html
+++ b/main/distributed.tensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -672,7 +672,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.__create_chunk_list__">
-<span class="sig-name descname"><span class="pre">__create_chunk_list__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.__create_chunk_list__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.__create_chunk_list__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__create_chunk_list__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.__create_chunk_list__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.__create_chunk_list__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of ChunkStorageMetadata, which is a dataclass that describes the size/offset of the local shard/replica
 on current rank. For DTensor, each rank will have a single local shard/replica, so the returned list usually only
 has one element.</p>
@@ -686,7 +686,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.from_local">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.from_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.from_local" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.from_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.from_local" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> from a local torch.Tensor on each rank
 according to the <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> and <code class="docutils literal notranslate"><span class="pre">placements</span></code> specified.</p>
 <dl class="field-list simple">
@@ -741,7 +741,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.full_tensor">
-<span class="sig-name descname"><span class="pre">full_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.full_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.full_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">full_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.full_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.full_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the full tensor of this DTensor. It will perform necessary collectives
 to gather the local tensors from other ranks in its DeviceMesh and concatenate
 them together. It’s a syntatic sugar of the following code:</p>
@@ -772,7 +772,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.redistribute">
-<span class="sig-name descname"><span class="pre">redistribute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.redistribute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.redistribute" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">redistribute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.redistribute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.redistribute" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">redistribute</span></code> performs necessary collective operations that redistribute the current
 DTensor from its current placements to a new placements, or from is current DeviceMesh
 to a new DeviceMesh. i.e. we can turn a Sharded DTensor to a Replicated DTensor by
@@ -825,7 +825,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.to_local">
-<span class="sig-name descname"><span class="pre">to_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.to_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.to_local" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.to_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.to_local" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the local tensor of this DTensor on its current rank. For sharding it returns
 a local shard of the logical tensor view, for replication it returns the replica on
 its current rank.</p>
@@ -893,7 +893,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 <p>DTensor supports the following types of <a class="reference internal" href="#torch.distributed.tensor.placement_types.Placement" title="torch.distributed.tensor.placement_types.Placement"><code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code></a> on each <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Shard">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/placement_types.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Shard" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Shard" title="Permalink to this definition">¶</a></dt>
 <dd><p>The <code class="docutils literal notranslate"><span class="pre">Shard(dim)</span></code> placement describes the DTensor sharding on tensor dimension
 <code class="docutils literal notranslate"><span class="pre">dim</span></code> over a corresponding <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension, where each rank on the
 DeviceMesh dimension only holds a shard/piece of the global Tensor. The
@@ -921,7 +921,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Replicate">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Replicate</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/placement_types.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Replicate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Replicate</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Replicate" title="Permalink to this definition">¶</a></dt>
 <dd><p>The <code class="docutils literal notranslate"><span class="pre">Replicate()</span></code> placement describes the DTensor replicating on a corresponding
 <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension, where each rank on the DeviceMesh dimension holds a
 replica of the global Tensor. The <code class="docutils literal notranslate"><span class="pre">Replicate</span></code> placement can be used by all
@@ -932,7 +932,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Partial">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Partial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduce_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/placement_types.py#L595"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Partial" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Partial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduce_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L595"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Partial" title="Permalink to this definition">¶</a></dt>
 <dd><p>The <code class="docutils literal notranslate"><span class="pre">Partial(reduce_op)</span></code> placement describes the DTensor that is pending
 reduction on a specified <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension, where each rank on the
 DeviceMesh dimension holds the partial value of the global Tensor. User can
@@ -961,7 +961,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Placement</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/placement_types.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Placement</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement" title="Permalink to this definition">¶</a></dt>
 <dd><p>The base class for the Placement type, where it describes how a DTensor is placed onto the
 <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code>. <code class="docutils literal notranslate"><span class="pre">Placement</span></code> and <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> together could describe the DTensor Layout.
 It is the base class of the three main DTensor Placement types: <code class="docutils literal notranslate"><span class="pre">Shard</span></code>, <code class="docutils literal notranslate"><span class="pre">Replicate</span></code>,
@@ -969,7 +969,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 <p>This class is not meant to be used directly, mainly served as a typing stub.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement.is_partial">
-<span class="sig-name descname"><span class="pre">is_partial</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/placement_types.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_partial" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_partial</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_partial" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
@@ -979,7 +979,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement.is_replicate">
-<span class="sig-name descname"><span class="pre">is_replicate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/placement_types.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_replicate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_replicate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_replicate" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
@@ -989,7 +989,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement.is_shard">
-<span class="sig-name descname"><span class="pre">is_shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/placement_types.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_shard" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_shard" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
@@ -1029,7 +1029,7 @@ <h3>Create DTensor from a logical torch.Tensor<a class="headerlink" href="#creat
 <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> s to comply with the single device semantic, which is critical for <strong>numerical correctness</strong>.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.distribute_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">distribute_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_data_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L641"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.distribute_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">distribute_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_data_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L641"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.distribute_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Distribute a leaf <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> (i.e. nn.Parameter/buffers) to the <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> according
 to the <code class="docutils literal notranslate"><span class="pre">placements</span></code> specified. The rank of <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> and <code class="docutils literal notranslate"><span class="pre">placements</span></code> must be the
 same. The <code class="docutils literal notranslate"><span class="pre">tensor</span></code> to distribute is the logical or “global” tensor, and the API would use
@@ -1081,7 +1081,7 @@ <h3>Create DTensor from a logical torch.Tensor<a class="headerlink" href="#creat
 sharding on the <code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Module</span></code> level</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.distribute_module">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">distribute_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partition_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.distribute_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">distribute_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partition_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.distribute_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function expose three functions to control the parameters/inputs/outputs of the module:</p>
 <p>1. To perform sharding on the module before runtime execution by specifying the
 <code class="docutils literal notranslate"><span class="pre">partition_fn</span></code> (i.e. allow user to convert Module parameters to <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a>
@@ -1129,7 +1129,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 specifying the <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code> for the <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> created:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.zeros">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">zeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L1266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.zeros" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">zeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.zeros" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with the scalar value 0.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1160,7 +1160,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.ones">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">ones</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.ones" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">ones</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.ones" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with the scalar value 1, with the shape defined
 by the variable argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
 <dl class="field-list simple">
@@ -1192,7 +1192,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.empty">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L1088"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.empty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1088"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.empty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with uninitialized data. The shape of the <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a>
 is defined by the variable argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
 <dl class="field-list simple">
@@ -1223,7 +1223,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.full">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">full</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L1131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.full" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">full</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.full" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with <code class="docutils literal notranslate"><span class="pre">fill_value</span></code> according to <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> and
 <code class="docutils literal notranslate"><span class="pre">placements</span></code>, with the shape defined by the argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
 <dl class="field-list simple">
@@ -1258,7 +1258,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.rand">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">rand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L1178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.rand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">rand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.rand" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with random numbers from a uniform distribution
 on the interval <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1)</span></code>. The shape of the tensor is defined by the variable
 argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
@@ -1291,7 +1291,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.randn">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">randn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/_api.py#L1222"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.randn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">randn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1222"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.randn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with random numbers from a normal distribution
 with mean 0 and variance 1. The shape of the tensor is defined by the variable
 argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
@@ -1360,7 +1360,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.generate_comm_debug_tracing_table">
-<span class="sig-name descname"><span class="pre">generate_comm_debug_tracing_table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.generate_comm_debug_tracing_table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/debug/_comm_mode.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.generate_comm_debug_tracing_table" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">generate_comm_debug_tracing_table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.generate_comm_debug_tracing_table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.generate_comm_debug_tracing_table" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates detailed table displaying operations and collective tracing information
 on a module level. Amount of information is dependent on noise_level</p>
 <ol class="arabic simple" start="0">
@@ -1373,7 +1373,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.generate_json_dump">
-<span class="sig-name descname"><span class="pre">generate_json_dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'comm_mode_log.json'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.generate_json_dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/debug/_comm_mode.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.generate_json_dump" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">generate_json_dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'comm_mode_log.json'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.generate_json_dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.generate_json_dump" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates json file used to build browser visual
 0. prints module-level collective counts
 1. prints dTensor operations not included in trivial operations
@@ -1383,7 +1383,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.get_comm_counts">
-<span class="sig-name descname"><span class="pre">get_comm_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_comm_counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/debug/_comm_mode.py#L570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_comm_counts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_comm_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_comm_counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_comm_counts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the communication counts as a dictionary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1397,7 +1397,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.get_parameter_info">
-<span class="sig-name descname"><span class="pre">get_parameter_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_parameter_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/debug/_comm_mode.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_parameter_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameter_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_parameter_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_parameter_info" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a>]]</p>
@@ -1407,7 +1407,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.get_sharding_info">
-<span class="sig-name descname"><span class="pre">get_sharding_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_sharding_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/debug/_comm_mode.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_sharding_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_sharding_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_sharding_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_sharding_info" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a>]]</p>
@@ -1417,7 +1417,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.get_total_counts">
-<span class="sig-name descname"><span class="pre">get_total_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_total_counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/debug/_comm_mode.py#L567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_total_counts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_total_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_total_counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_total_counts" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
@@ -1427,7 +1427,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.log_comm_debug_tracing_table_to_file">
-<span class="sig-name descname"><span class="pre">log_comm_debug_tracing_table_to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'comm_mode_log.txt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.log_comm_debug_tracing_table_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/debug/_comm_mode.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.log_comm_debug_tracing_table_to_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_comm_debug_tracing_table_to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'comm_mode_log.txt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.log_comm_debug_tracing_table_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.log_comm_debug_tracing_table_to_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Alternative to console CommDebugMode output, writes to file specified by the user</p>
 </dd></dl>
 
@@ -1436,7 +1436,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 <p>To visualize the sharding of a DTensor that have less than 3 dimensions, DTensor provides <a class="reference internal" href="#torch.distributed.tensor.debug.visualize_sharding" title="torch.distributed.tensor.debug.visualize_sharding"><code class="xref py py-meth docutils literal notranslate"><span class="pre">visualize_sharding()</span></code></a>:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.visualize_sharding">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.debug.</span></span><span class="sig-name descname"><span class="pre">visualize_sharding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/debug/_visualize_sharding.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.visualize_sharding" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.debug.</span></span><span class="sig-name descname"><span class="pre">visualize_sharding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_visualize_sharding.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.visualize_sharding" title="Permalink to this definition">¶</a></dt>
 <dd><p>Visualizes sharding in the terminal for <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> that are 1D or 2D.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1453,7 +1453,7 @@ <h2>Experimental Features<a class="headerlink" href="#experimental-features" tit
 these features.</p>
 <span class="target" id="module-torch.distributed.tensor.experimental"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.experimental.context_parallel">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">context_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_seq_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_restore_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/experimental/_attention.py#L1202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.context_parallel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">context_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_seq_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_restore_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/experimental/_attention.py#L1202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.context_parallel" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">context_parallel</span></code> is an experimental API to enable context
 parallelism (CP). This API performs two actions: 1) patch the SDPA
 (<code class="docutils literal notranslate"><span class="pre">torch.nn.functional.scaled_dot_product_attention</span></code>) with the CP-enabled
@@ -1492,7 +1492,7 @@ <h2>Experimental Features<a class="headerlink" href="#experimental-features" tit
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.experimental.local_map">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">local_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_placements</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redistribute_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/experimental/_func_map.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.local_map" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">local_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_placements</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redistribute_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/experimental/_func_map.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.local_map" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.distributed.tensor.experimental.local_map" title="torch.distributed.tensor.experimental.local_map"><code class="xref py py-meth docutils literal notranslate"><span class="pre">local_map()</span></code></a> is an experimental API that allows users to pass <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> s
 to a function that is written to be applied on <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> s. It is done by extracting
 the local components of <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>, call the function, and wrap the outputs to
@@ -1586,7 +1586,7 @@ <h2>Experimental Features<a class="headerlink" href="#experimental-features" tit
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.experimental.register_sharding">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">register_sharding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/experimental/_register_sharding.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.register_sharding" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">register_sharding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/experimental/_register_sharding.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.register_sharding" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.distributed.tensor.experimental.register_sharding" title="torch.distributed.tensor.experimental.register_sharding"><code class="xref py py-meth docutils literal notranslate"><span class="pre">register_sharding()</span></code></a> is an experimental API that allows users to register sharding
 strategies for an operator when the tensor inputs and outputs are DTensor.
 It can be useful when: (1) there doesn’t exist a default sharding strategy for <code class="docutils literal notranslate"><span class="pre">op</span></code>,
diff --git a/main/distributed.tensor.parallel.html b/main/distributed.tensor.parallel.html
index 64c6a292444..c26bb65eb5d 100644
--- a/main/distributed.tensor.parallel.html
+++ b/main/distributed.tensor.parallel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -618,7 +618,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 <p>The entrypoint to parallelize your <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> using Tensor Parallelism is:</p>
 <span class="target" id="module-torch.distributed.tensor.parallel"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.parallelize_module">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">parallelize_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallelize_plan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_data_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/api.html#parallelize_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/parallel/api.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.parallelize_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">parallelize_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallelize_plan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_data_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/api.html#parallelize_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/api.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.parallelize_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Tensor Parallelism in PyTorch by parallelizing modules or sub-modules based on a user-specified plan.</p>
 <p>We parallelize module or sub_modules based on a parallelize_plan. The parallelize_plan contains
 <code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code>, which indicates how user wants the module or sub_module
@@ -677,7 +677,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 <p>Tensor Parallelism supports the following parallel styles:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.ColwiseParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">ColwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#ColwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/parallel/style.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.ColwiseParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">ColwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#ColwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.ColwiseParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Partition a compatible nn.Module in a column-wise fashion. Currently supports nn.Linear and nn.Embedding.
 Users can compose it together with RowwiseParallel to achieve the sharding of more complicated modules.
 (i.e. MLP, Attention)</p>
@@ -721,7 +721,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.RowwiseParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">RowwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#RowwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/parallel/style.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.RowwiseParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">RowwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#RowwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.RowwiseParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Partition a compatible nn.Module in a row-wise fashion. Currently supports nn.Linear and nn.Embedding.
 Users can compose it with ColwiseParallel to achieve the sharding of more complicated modules.
 (i.e. MLP, Attention)</p>
@@ -759,7 +759,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.SequenceParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">SequenceParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sequence_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#SequenceParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/parallel/style.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.SequenceParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">SequenceParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sequence_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#SequenceParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.SequenceParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>SequenceParallel replicates a compatible <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> parameters and runs the sharded computation with
 input sharded on the sequence dimension. This currently supports <code class="docutils literal notranslate"><span class="pre">nn.LayerNorm</span></code>, <code class="docutils literal notranslate"><span class="pre">nn.Dropout</span></code>, and the
 <a class="reference external" href="https://github.com/facebookresearch/llama/blob/main/llama/model.py#L34">RMSNorm python implementation</a></p>
@@ -813,7 +813,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 the <code class="docutils literal notranslate"><span class="pre">parallelize_plan</span></code> when calling <code class="docutils literal notranslate"><span class="pre">parallelize_module</span></code>:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.PrepareModuleInput">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleInput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_kwarg_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_input_kwarg_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleInput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/parallel/style.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleInput" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleInput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_kwarg_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_input_kwarg_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleInput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleInput" title="Permalink to this definition">¶</a></dt>
 <dd><p>Configure the nn.Module’s inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
 <code class="docutils literal notranslate"><span class="pre">input_layouts</span></code>, and perform layout redistribution according to the <code class="docutils literal notranslate"><span class="pre">desired_input_layouts</span></code>.</p>
 <dl class="field-list simple">
@@ -863,7 +863,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.PrepareModuleOutput">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/parallel/style.py#L558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleOutput" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleOutput" title="Permalink to this definition">¶</a></dt>
 <dd><p>Configure the nn.Module’s outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
 <code class="docutils literal notranslate"><span class="pre">output_layouts</span></code>, and perform layout redistribution according to the <code class="docutils literal notranslate"><span class="pre">desired_output_layouts</span></code>.</p>
 <dl class="field-list simple">
@@ -921,7 +921,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 <p>Parallelized cross-entropy loss computation (loss parallelism), is supported via the following context manager:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.loss_parallel">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">loss_parallel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/loss.html#loss_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/tensor/parallel/loss.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.loss_parallel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">loss_parallel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/loss.html#loss_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/loss.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.loss_parallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>A context manager that enables loss parallelism, where efficient parallelized loss computation
 can be performed when the input is sharded on the class dimension. Currently only the cross-entropy
 loss is supported.</p>
diff --git a/main/distributions.html b/main/distributions.html
index 4c363816789..3088d84b4b0 100644
--- a/main/distributions.html
+++ b/main/distributions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -670,7 +670,7 @@ <h2>Pathwise derivative<a class="headerlink" href="#pathwise-derivative" title="
 <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="#distribution" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.distribution.</span></span><span class="sig-name descname"><span class="pre">Distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.distribution.</span></span><span class="sig-name descname"><span class="pre">Distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Distribution is the abstract base class for probability distributions.</p>
 <dl class="field-list simple">
@@ -692,7 +692,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.cdf" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the cumulative density/mass function evaluated at
 <cite>value</cite>.</p>
 <dl class="field-list simple">
@@ -707,7 +707,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.entropy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns entropy of distribution, batched over batch_shape.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -721,7 +721,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns tensor containing all values supported by a discrete
 distribution. The result will enumerate over dimension 0, so the shape
 of the result will be <cite>(cardinality,) + batch_shape + event_shape</cite>
@@ -754,7 +754,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.expand" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a new distribution instance (or populates an existing instance
 provided by a derived class) with batch dimensions expanded to
 <cite>batch_shape</cite>. This method calls <a class="reference internal" href="generated/torch.Tensor.expand.html#torch.Tensor.expand" title="torch.Tensor.expand"><code class="xref py py-class docutils literal notranslate"><span class="pre">expand</span></code></a> on
@@ -779,7 +779,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.icdf" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the inverse cumulative density/mass function evaluated at
 <cite>value</cite>.</p>
 <dl class="field-list simple">
@@ -794,7 +794,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the log of the probability density/mass function evaluated at
 <cite>value</cite>.</p>
 <dl class="field-list simple">
@@ -821,7 +821,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.perplexity">
-<span class="sig-name descname"><span class="pre">perplexity</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.perplexity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.perplexity" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">perplexity</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.perplexity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.perplexity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns perplexity of distribution, batched over batch_shape.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -835,7 +835,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a sample_shape shaped reparameterized sample or sample_shape
 shaped batch of reparameterized samples if the distribution parameters
 are batched.</p>
@@ -848,7 +848,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.sample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a sample_shape shaped sample or sample_shape shaped batch of
 samples if the distribution parameters are batched.</p>
 <dl class="field-list simple">
@@ -860,7 +860,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.sample_n">
-<span class="sig-name descname"><span class="pre">sample_n</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.sample_n"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.sample_n" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample_n</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.sample_n"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.sample_n" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates n samples or n batches of samples if the distribution
 parameters are batched.</p>
 <dl class="field-list simple">
@@ -872,7 +872,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.set_default_validate_args">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_default_validate_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.set_default_validate_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/distribution.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.set_default_validate_args" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_default_validate_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.set_default_validate_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.set_default_validate_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether validation is enabled or disabled.</p>
 <p>The default behavior mimics Python’s <code class="docutils literal notranslate"><span class="pre">assert</span></code> statement: validation
 is on by default, but is disabled if Python is run in optimized mode
@@ -911,7 +911,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 <h2><span class="hidden-section">ExponentialFamily</span><a class="headerlink" href="#exponentialfamily" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.exp_family.ExponentialFamily">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.exp_family.</span></span><span class="sig-name descname"><span class="pre">ExponentialFamily</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exp_family.html#ExponentialFamily"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/exp_family.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exp_family.ExponentialFamily" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.exp_family.</span></span><span class="sig-name descname"><span class="pre">ExponentialFamily</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exp_family.html#ExponentialFamily"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exp_family.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exp_family.ExponentialFamily" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>ExponentialFamily is the abstract base class for probability distributions belonging to an
 exponential family, whose probability mass/density function has the form is defined below</p>
@@ -931,7 +931,7 @@ <h2><span class="hidden-section">ExponentialFamily</span><a class="headerlink" h
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exp_family.ExponentialFamily.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exp_family.html#ExponentialFamily.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/exp_family.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exp_family.ExponentialFamily.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exp_family.html#ExponentialFamily.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exp_family.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exp_family.ExponentialFamily.entropy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method to compute the entropy using Bregman divergence of the log normalizer.</p>
 </dd></dl>
 
@@ -942,7 +942,7 @@ <h2><span class="hidden-section">ExponentialFamily</span><a class="headerlink" h
 <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#bernoulli" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.bernoulli.</span></span><span class="sig-name descname"><span class="pre">Bernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/bernoulli.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.bernoulli.</span></span><span class="sig-name descname"><span class="pre">Bernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Bernoulli distribution parameterized by <a class="reference internal" href="#torch.distributions.bernoulli.Bernoulli.probs" title="torch.distributions.bernoulli.Bernoulli.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a>
 or <a class="reference internal" href="#torch.distributions.bernoulli.Bernoulli.logits" title="torch.distributions.bernoulli.Bernoulli.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both).</p>
@@ -969,17 +969,17 @@ <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#be
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/bernoulli.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/bernoulli.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/bernoulli.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -989,7 +989,7 @@ <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#be
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/bernoulli.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1019,7 +1019,7 @@ <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#be
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/bernoulli.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1039,7 +1039,7 @@ <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#be
 <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.beta.</span></span><span class="sig-name descname"><span class="pre">Beta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/beta.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.beta.</span></span><span class="sig-name descname"><span class="pre">Beta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Beta distribution parameterized by <a class="reference internal" href="#torch.distributions.beta.Beta.concentration1" title="torch.distributions.beta.Beta.concentration1"><code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration1</span></code></a> and <a class="reference internal" href="#torch.distributions.beta.Beta.concentration0" title="torch.distributions.beta.Beta.concentration0"><code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration0</span></code></a>.</p>
 <p>Example:</p>
@@ -1075,12 +1075,12 @@ <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/beta.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/beta.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1090,7 +1090,7 @@ <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/beta.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1105,7 +1105,7 @@ <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/beta.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1130,7 +1130,7 @@ <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" t
 <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#binomial" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.binomial.</span></span><span class="sig-name descname"><span class="pre">Binomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/binomial.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.binomial.</span></span><span class="sig-name descname"><span class="pre">Binomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Binomial distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">total_count</span></code> and
 either <a class="reference internal" href="#torch.distributions.binomial.Binomial.probs" title="torch.distributions.binomial.Binomial.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or <a class="reference internal" href="#torch.distributions.binomial.Binomial.logits" title="torch.distributions.binomial.Binomial.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both). <code class="xref py py-attr docutils literal notranslate"><span class="pre">total_count</span></code> must be
@@ -1162,17 +1162,17 @@ <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#bin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/binomial.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/binomial.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/binomial.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1182,7 +1182,7 @@ <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#bin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/binomial.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1212,7 +1212,7 @@ <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#bin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/binomial.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1237,7 +1237,7 @@ <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#bin
 <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#categorical" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.categorical.</span></span><span class="sig-name descname"><span class="pre">Categorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/categorical.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.categorical.</span></span><span class="sig-name descname"><span class="pre">Categorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a categorical distribution parameterized by either <a class="reference internal" href="#torch.distributions.categorical.Categorical.probs" title="torch.distributions.categorical.Categorical.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or
 <a class="reference internal" href="#torch.distributions.categorical.Categorical.logits" title="torch.distributions.categorical.Categorical.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both).</p>
@@ -1283,17 +1283,17 @@ <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/categorical.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/categorical.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/categorical.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1303,7 +1303,7 @@ <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/categorical.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1333,7 +1333,7 @@ <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/categorical.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1358,7 +1358,7 @@ <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#
 <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauchy" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.cauchy.</span></span><span class="sig-name descname"><span class="pre">Cauchy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/cauchy.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.cauchy.</span></span><span class="sig-name descname"><span class="pre">Cauchy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Samples from a Cauchy (Lorentz) distribution. The distribution of the ratio of
 independent normally distributed random variables with means <cite>0</cite> follows a
@@ -1384,17 +1384,17 @@ <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauch
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/cauchy.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/cauchy.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/cauchy.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1404,12 +1404,12 @@ <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauch
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/cauchy.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/cauchy.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1424,7 +1424,7 @@ <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauch
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/cauchy.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1449,7 +1449,7 @@ <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauch
 <h2><span class="hidden-section">Chi2</span><a class="headerlink" href="#chi2" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.chi2.Chi2">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.chi2.</span></span><span class="sig-name descname"><span class="pre">Chi2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/chi2.html#Chi2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/chi2.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.chi2.Chi2" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.chi2.</span></span><span class="sig-name descname"><span class="pre">Chi2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/chi2.html#Chi2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/chi2.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.chi2.Chi2" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.gamma.Gamma" title="torch.distributions.gamma.Gamma"><code class="xref py py-class docutils literal notranslate"><span class="pre">Gamma</span></code></a></p>
 <p>Creates a Chi-squared distribution parameterized by shape parameter <a class="reference internal" href="#torch.distributions.chi2.Chi2.df" title="torch.distributions.chi2.Chi2.df"><code class="xref py py-attr docutils literal notranslate"><span class="pre">df</span></code></a>.
 This is exactly equivalent to <code class="docutils literal notranslate"><span class="pre">Gamma(alpha=0.5*df,</span> <span class="pre">beta=0.5)</span></code></p>
@@ -1476,7 +1476,7 @@ <h2><span class="hidden-section">Chi2</span><a class="headerlink" href="#chi2" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.chi2.Chi2.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/chi2.html#Chi2.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/chi2.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.chi2.Chi2.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/chi2.html#Chi2.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/chi2.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.chi2.Chi2.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
@@ -1486,7 +1486,7 @@ <h2><span class="hidden-section">Chi2</span><a class="headerlink" href="#chi2" t
 <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink" href="#continuousbernoulli" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.continuous_bernoulli.</span></span><span class="sig-name descname"><span class="pre">ContinuousBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.499,</span> <span class="pre">0.501)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/continuous_bernoulli.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.continuous_bernoulli.</span></span><span class="sig-name descname"><span class="pre">ContinuousBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.499,</span> <span class="pre">0.501)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a continuous Bernoulli distribution parameterized by <a class="reference internal" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.probs" title="torch.distributions.continuous_bernoulli.ContinuousBernoulli.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a>
 or <a class="reference internal" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits" title="torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both).</p>
@@ -1519,17 +1519,17 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/continuous_bernoulli.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/continuous_bernoulli.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/continuous_bernoulli.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1539,12 +1539,12 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/continuous_bernoulli.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/continuous_bernoulli.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1569,7 +1569,7 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/continuous_bernoulli.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1579,7 +1579,7 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/continuous_bernoulli.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1604,7 +1604,7 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#dirichlet" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.dirichlet.</span></span><span class="sig-name descname"><span class="pre">Dirichlet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/dirichlet.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.dirichlet.</span></span><span class="sig-name descname"><span class="pre">Dirichlet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Dirichlet distribution parameterized by concentration <code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration</span></code>.</p>
 <p>Example:</p>
@@ -1626,12 +1626,12 @@ <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#di
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/dirichlet.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/dirichlet.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1641,7 +1641,7 @@ <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#di
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/dirichlet.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1656,7 +1656,7 @@ <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#di
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/dirichlet.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1681,7 +1681,7 @@ <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#di
 <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#exponential" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.exponential.</span></span><span class="sig-name descname"><span class="pre">Exponential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/exponential.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.exponential.</span></span><span class="sig-name descname"><span class="pre">Exponential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Exponential distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">rate</span></code>.</p>
 <p>Example:</p>
@@ -1702,17 +1702,17 @@ <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/exponential.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/exponential.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/exponential.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1722,12 +1722,12 @@ <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/exponential.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/exponential.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1742,7 +1742,7 @@ <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/exponential.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1772,7 +1772,7 @@ <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#
 <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href="#fishersnedecor" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.fishersnedecor.FisherSnedecor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.fishersnedecor.</span></span><span class="sig-name descname"><span class="pre">FisherSnedecor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/fishersnedecor.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.fishersnedecor.</span></span><span class="sig-name descname"><span class="pre">FisherSnedecor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/fishersnedecor.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Fisher-Snedecor distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">df1</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">df2</span></code>.</p>
 <p>Example:</p>
@@ -1796,7 +1796,7 @@ <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.fishersnedecor.FisherSnedecor.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/fishersnedecor.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/fishersnedecor.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1806,7 +1806,7 @@ <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.fishersnedecor.FisherSnedecor.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/fishersnedecor.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/fishersnedecor.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1821,7 +1821,7 @@ <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.fishersnedecor.FisherSnedecor.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/fishersnedecor.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/fishersnedecor.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1846,7 +1846,7 @@ <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href
 <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.gamma.</span></span><span class="sig-name descname"><span class="pre">Gamma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gamma.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.gamma.</span></span><span class="sig-name descname"><span class="pre">Gamma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Gamma distribution parameterized by shape <code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">rate</span></code>.</p>
 <p>Example:</p>
@@ -1872,17 +1872,17 @@ <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gamma.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gamma.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gamma.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1892,7 +1892,7 @@ <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gamma.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1907,7 +1907,7 @@ <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gamma.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1932,7 +1932,7 @@ <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma"
 <h2><span class="hidden-section">Geometric</span><a class="headerlink" href="#geometric" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.geometric.</span></span><span class="sig-name descname"><span class="pre">Geometric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/geometric.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.geometric.</span></span><span class="sig-name descname"><span class="pre">Geometric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Geometric distribution parameterized by <a class="reference internal" href="#torch.distributions.geometric.Geometric.probs" title="torch.distributions.geometric.Geometric.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a>,
 where <a class="reference internal" href="#torch.distributions.geometric.Geometric.probs" title="torch.distributions.geometric.Geometric.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> is the probability of success of Bernoulli trials.</p>
@@ -1964,17 +1964,17 @@ <h2><span class="hidden-section">Geometric</span><a class="headerlink" href="#ge
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/geometric.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/geometric.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/geometric.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1999,7 +1999,7 @@ <h2><span class="hidden-section">Geometric</span><a class="headerlink" href="#ge
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/geometric.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2019,7 +2019,7 @@ <h2><span class="hidden-section">Geometric</span><a class="headerlink" href="#ge
 <h2><span class="hidden-section">Gumbel</span><a class="headerlink" href="#gumbel" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.gumbel.Gumbel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.gumbel.</span></span><span class="sig-name descname"><span class="pre">Gumbel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gumbel.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.gumbel.</span></span><span class="sig-name descname"><span class="pre">Gumbel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gumbel.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Samples from a Gumbel Distribution.</p>
 <p>Examples:</p>
@@ -2043,17 +2043,17 @@ <h2><span class="hidden-section">Gumbel</span><a class="headerlink" href="#gumbe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gumbel.Gumbel.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gumbel.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gumbel.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gumbel.Gumbel.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gumbel.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gumbel.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gumbel.Gumbel.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/gumbel.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gumbel.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2088,7 +2088,7 @@ <h2><span class="hidden-section">Gumbel</span><a class="headerlink" href="#gumbe
 <h2><span class="hidden-section">HalfCauchy</span><a class="headerlink" href="#halfcauchy" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.half_cauchy.</span></span><span class="sig-name descname"><span class="pre">HalfCauchy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_cauchy.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.half_cauchy.</span></span><span class="sig-name descname"><span class="pre">HalfCauchy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a half-Cauchy distribution parameterized by <cite>scale</cite> where:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">~</span> <span class="n">Cauchy</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
@@ -2113,17 +2113,17 @@ <h2><span class="hidden-section">HalfCauchy</span><a class="headerlink" href="#h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_cauchy.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_cauchy.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_cauchy.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2133,12 +2133,12 @@ <h2><span class="hidden-section">HalfCauchy</span><a class="headerlink" href="#h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_cauchy.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_cauchy.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2173,7 +2173,7 @@ <h2><span class="hidden-section">HalfCauchy</span><a class="headerlink" href="#h
 <h2><span class="hidden-section">HalfNormal</span><a class="headerlink" href="#halfnormal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.half_normal.</span></span><span class="sig-name descname"><span class="pre">HalfNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_normal.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.half_normal.</span></span><span class="sig-name descname"><span class="pre">HalfNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a half-normal distribution parameterized by <cite>scale</cite> where:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">~</span> <span class="n">Normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
@@ -2198,17 +2198,17 @@ <h2><span class="hidden-section">HalfNormal</span><a class="headerlink" href="#h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_normal.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_normal.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_normal.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2218,12 +2218,12 @@ <h2><span class="hidden-section">HalfNormal</span><a class="headerlink" href="#h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_normal.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/half_normal.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2258,7 +2258,7 @@ <h2><span class="hidden-section">HalfNormal</span><a class="headerlink" href="#h
 <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#independent" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.independent.</span></span><span class="sig-name descname"><span class="pre">Independent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reinterpreted_batch_ndims</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/independent.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.independent.</span></span><span class="sig-name descname"><span class="pre">Independent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reinterpreted_batch_ndims</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Reinterprets some of the batch dims of a distribution as event dims.</p>
 <p>This is mainly useful for changing the shape of the result of
@@ -2297,17 +2297,17 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/independent.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/independent.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/independent.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2322,7 +2322,7 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/independent.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2337,7 +2337,7 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/independent.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -2347,7 +2347,7 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/independent.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.sample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -2377,7 +2377,7 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 <h2><span class="hidden-section">InverseGamma</span><a class="headerlink" href="#inversegamma" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.inverse_gamma.InverseGamma">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.inverse_gamma.</span></span><span class="sig-name descname"><span class="pre">InverseGamma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/inverse_gamma.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.inverse_gamma.</span></span><span class="sig-name descname"><span class="pre">InverseGamma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/inverse_gamma.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates an inverse gamma distribution parameterized by <a class="reference internal" href="#torch.distributions.inverse_gamma.InverseGamma.concentration" title="torch.distributions.inverse_gamma.InverseGamma.concentration"><code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration</span></code></a> and <a class="reference internal" href="#torch.distributions.inverse_gamma.InverseGamma.rate" title="torch.distributions.inverse_gamma.InverseGamma.rate"><code class="xref py py-attr docutils literal notranslate"><span class="pre">rate</span></code></a>
 where:</p>
@@ -2413,12 +2413,12 @@ <h2><span class="hidden-section">InverseGamma</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.inverse_gamma.InverseGamma.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/inverse_gamma.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/inverse_gamma.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.inverse_gamma.InverseGamma.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/inverse_gamma.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/inverse_gamma.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2458,7 +2458,7 @@ <h2><span class="hidden-section">InverseGamma</span><a class="headerlink" href="
 <h2><span class="hidden-section">Kumaraswamy</span><a class="headerlink" href="#kumaraswamy" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.kumaraswamy.Kumaraswamy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.kumaraswamy.</span></span><span class="sig-name descname"><span class="pre">Kumaraswamy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/kumaraswamy.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.kumaraswamy.</span></span><span class="sig-name descname"><span class="pre">Kumaraswamy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kumaraswamy.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Samples from a Kumaraswamy distribution.</p>
 <p>Example:</p>
@@ -2484,12 +2484,12 @@ <h2><span class="hidden-section">Kumaraswamy</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.kumaraswamy.Kumaraswamy.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/kumaraswamy.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kumaraswamy.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.kumaraswamy.Kumaraswamy.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/kumaraswamy.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kumaraswamy.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2524,7 +2524,7 @@ <h2><span class="hidden-section">Kumaraswamy</span><a class="headerlink" href="#
 <h2><span class="hidden-section">LKJCholesky</span><a class="headerlink" href="#lkjcholesky" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.lkj_cholesky.LKJCholesky">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.lkj_cholesky.</span></span><span class="sig-name descname"><span class="pre">LKJCholesky</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/lkj_cholesky.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.lkj_cholesky.</span></span><span class="sig-name descname"><span class="pre">LKJCholesky</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lkj_cholesky.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>LKJ distribution for lower Cholesky factor of correlation matrices.
 The distribution is controlled by <code class="docutils literal notranslate"><span class="pre">concentration</span></code> parameter <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>η</mi></mrow><annotation encoding="application/x-tex">\eta</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">η</span></span></span></span></span>
@@ -2569,17 +2569,17 @@ <h2><span class="hidden-section">LKJCholesky</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lkj_cholesky.LKJCholesky.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/lkj_cholesky.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lkj_cholesky.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lkj_cholesky.LKJCholesky.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/lkj_cholesky.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lkj_cholesky.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lkj_cholesky.LKJCholesky.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/lkj_cholesky.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lkj_cholesky.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2594,7 +2594,7 @@ <h2><span class="hidden-section">LKJCholesky</span><a class="headerlink" href="#
 <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#laplace" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.laplace.</span></span><span class="sig-name descname"><span class="pre">Laplace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/laplace.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.laplace.</span></span><span class="sig-name descname"><span class="pre">Laplace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Laplace distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">loc</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code>.</p>
 <p>Example:</p>
@@ -2618,17 +2618,17 @@ <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#lapl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/laplace.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/laplace.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/laplace.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2638,12 +2638,12 @@ <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#lapl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/laplace.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/laplace.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2658,7 +2658,7 @@ <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#lapl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/laplace.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -2688,7 +2688,7 @@ <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#lapl
 <h2><span class="hidden-section">LogNormal</span><a class="headerlink" href="#lognormal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.log_normal.LogNormal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.log_normal.</span></span><span class="sig-name descname"><span class="pre">LogNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/log_normal.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.log_normal.</span></span><span class="sig-name descname"><span class="pre">LogNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/log_normal.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a log-normal distribution parameterized by
 <a class="reference internal" href="#torch.distributions.log_normal.LogNormal.loc" title="torch.distributions.log_normal.LogNormal.loc"><code class="xref py py-attr docutils literal notranslate"><span class="pre">loc</span></code></a> and <a class="reference internal" href="#torch.distributions.log_normal.LogNormal.scale" title="torch.distributions.log_normal.LogNormal.scale"><code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code></a> where:</p>
@@ -2717,12 +2717,12 @@ <h2><span class="hidden-section">LogNormal</span><a class="headerlink" href="#lo
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.log_normal.LogNormal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/log_normal.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/log_normal.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.log_normal.LogNormal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/log_normal.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/log_normal.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2767,7 +2767,7 @@ <h2><span class="hidden-section">LogNormal</span><a class="headerlink" href="#lo
 <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="headerlink" href="#lowrankmultivariatenormal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.lowrank_multivariate_normal.</span></span><span class="sig-name descname"><span class="pre">LowRankMultivariateNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov_factor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov_diag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/lowrank_multivariate_normal.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.lowrank_multivariate_normal.</span></span><span class="sig-name descname"><span class="pre">LowRankMultivariateNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov_factor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov_diag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a multivariate normal distribution with covariance matrix having a low-rank form
 parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">cov_factor</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">cov_diag</span></code>:</p>
@@ -2814,12 +2814,12 @@ <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="heade
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/lowrank_multivariate_normal.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/lowrank_multivariate_normal.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2829,7 +2829,7 @@ <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="heade
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/lowrank_multivariate_normal.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2849,7 +2849,7 @@ <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="heade
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/lowrank_multivariate_normal.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -2879,7 +2879,7 @@ <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="heade
 <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" href="#mixturesamefamily" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.mixture_same_family.</span></span><span class="sig-name descname"><span class="pre">MixtureSameFamily</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mixture_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">component_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/mixture_same_family.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.mixture_same_family.</span></span><span class="sig-name descname"><span class="pre">MixtureSameFamily</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mixture_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">component_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>The <cite>MixtureSameFamily</cite> distribution implements a (batch of) mixture
 distribution where all component are from different parameterizations of
@@ -2930,7 +2930,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/mixture_same_family.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2940,7 +2940,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/mixture_same_family.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2950,7 +2950,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/mixture_same_family.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2965,7 +2965,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/mixture_same_family.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2990,7 +2990,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 <h2><span class="hidden-section">Multinomial</span><a class="headerlink" href="#multinomial" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.multinomial.</span></span><span class="sig-name descname"><span class="pre">Multinomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multinomial.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.multinomial.</span></span><span class="sig-name descname"><span class="pre">Multinomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Multinomial distribution parameterized by <a class="reference internal" href="#torch.distributions.multinomial.Multinomial.total_count" title="torch.distributions.multinomial.Multinomial.total_count"><code class="xref py py-attr docutils literal notranslate"><span class="pre">total_count</span></code></a> and
 either <a class="reference internal" href="#torch.distributions.multinomial.Multinomial.probs" title="torch.distributions.multinomial.Multinomial.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or <a class="reference internal" href="#torch.distributions.multinomial.Multinomial.logits" title="torch.distributions.multinomial.Multinomial.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both). The innermost dimension of
@@ -3038,17 +3038,17 @@ <h2><span class="hidden-section">Multinomial</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multinomial.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multinomial.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multinomial.py#L128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3073,7 +3073,7 @@ <h2><span class="hidden-section">Multinomial</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multinomial.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3103,7 +3103,7 @@ <h2><span class="hidden-section">Multinomial</span><a class="headerlink" href="#
 <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink" href="#multivariatenormal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.multivariate_normal.</span></span><span class="sig-name descname"><span class="pre">MultivariateNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_tril</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multivariate_normal.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.multivariate_normal.</span></span><span class="sig-name descname"><span class="pre">MultivariateNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_tril</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a multivariate normal (also called Gaussian) distribution
 parameterized by a mean vector and a covariance matrix.</p>
@@ -3151,12 +3151,12 @@ <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multivariate_normal.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multivariate_normal.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3166,7 +3166,7 @@ <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multivariate_normal.py#L248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3186,7 +3186,7 @@ <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/multivariate_normal.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3216,7 +3216,7 @@ <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink"
 <h2><span class="hidden-section">NegativeBinomial</span><a class="headerlink" href="#negativebinomial" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.negative_binomial.NegativeBinomial">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.negative_binomial.</span></span><span class="sig-name descname"><span class="pre">NegativeBinomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/negative_binomial.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.negative_binomial.</span></span><span class="sig-name descname"><span class="pre">NegativeBinomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/negative_binomial.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Negative Binomial distribution, i.e. distribution
 of the number of successful independent and identical Bernoulli trials
@@ -3240,12 +3240,12 @@ <h2><span class="hidden-section">NegativeBinomial</span><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.negative_binomial.NegativeBinomial.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/negative_binomial.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/negative_binomial.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.negative_binomial.NegativeBinomial.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/negative_binomial.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/negative_binomial.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3275,7 +3275,7 @@ <h2><span class="hidden-section">NegativeBinomial</span><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.negative_binomial.NegativeBinomial.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/negative_binomial.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/negative_binomial.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3295,7 +3295,7 @@ <h2><span class="hidden-section">NegativeBinomial</span><a class="headerlink" hr
 <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#normal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.normal.</span></span><span class="sig-name descname"><span class="pre">Normal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/normal.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.normal.</span></span><span class="sig-name descname"><span class="pre">Normal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a normal (also called Gaussian) distribution parameterized by
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">loc</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code>.</p>
@@ -3321,17 +3321,17 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/normal.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/normal.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/normal.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3341,12 +3341,12 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/normal.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/normal.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3361,7 +3361,7 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/normal.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3371,7 +3371,7 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/normal.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3396,7 +3396,7 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" href="#onehotcategorical" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.one_hot_categorical.</span></span><span class="sig-name descname"><span class="pre">OneHotCategorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/one_hot_categorical.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.one_hot_categorical.</span></span><span class="sig-name descname"><span class="pre">OneHotCategorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a one-hot categorical distribution parameterized by <a class="reference internal" href="#torch.distributions.one_hot_categorical.OneHotCategorical.probs" title="torch.distributions.one_hot_categorical.OneHotCategorical.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or
 <a class="reference internal" href="#torch.distributions.one_hot_categorical.OneHotCategorical.logits" title="torch.distributions.one_hot_categorical.OneHotCategorical.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a>.</p>
@@ -3434,17 +3434,17 @@ <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/one_hot_categorical.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/one_hot_categorical.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/one_hot_categorical.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3454,7 +3454,7 @@ <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/one_hot_categorical.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3484,7 +3484,7 @@ <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/one_hot_categorical.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3504,7 +3504,7 @@ <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" h
 <h2><span class="hidden-section">Pareto</span><a class="headerlink" href="#pareto" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.pareto.Pareto">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.pareto.</span></span><span class="sig-name descname"><span class="pre">Pareto</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/pareto.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.pareto.</span></span><span class="sig-name descname"><span class="pre">Pareto</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/pareto.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Samples from a Pareto Type 1 distribution.</p>
 <p>Example:</p>
@@ -3528,7 +3528,7 @@ <h2><span class="hidden-section">Pareto</span><a class="headerlink" href="#paret
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.pareto.Pareto.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/pareto.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/pareto.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto.entropy" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3538,7 +3538,7 @@ <h2><span class="hidden-section">Pareto</span><a class="headerlink" href="#paret
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.pareto.Pareto.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/pareto.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/pareto.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto.expand" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="#torch.distributions.pareto.Pareto" title="torch.distributions.pareto.Pareto"><em>Pareto</em></a></p>
@@ -3578,7 +3578,7 @@ <h2><span class="hidden-section">Pareto</span><a class="headerlink" href="#paret
 <h2><span class="hidden-section">Poisson</span><a class="headerlink" href="#poisson" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.poisson.Poisson">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.poisson.</span></span><span class="sig-name descname"><span class="pre">Poisson</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/poisson.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.poisson.</span></span><span class="sig-name descname"><span class="pre">Poisson</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/poisson.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Poisson distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">rate</span></code>, the rate parameter.</p>
 <p>Samples are nonnegative integers, with a pmf given by</p>
@@ -3603,12 +3603,12 @@ <h2><span class="hidden-section">Poisson</span><a class="headerlink" href="#pois
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.poisson.Poisson.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/poisson.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/poisson.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.poisson.Poisson.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/poisson.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/poisson.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3623,7 +3623,7 @@ <h2><span class="hidden-section">Poisson</span><a class="headerlink" href="#pois
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.poisson.Poisson.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/poisson.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/poisson.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3643,7 +3643,7 @@ <h2><span class="hidden-section">Poisson</span><a class="headerlink" href="#pois
 <h2><span class="hidden-section">RelaxedBernoulli</span><a class="headerlink" href="#relaxedbernoulli" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.RelaxedBernoulli">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_bernoulli.</span></span><span class="sig-name descname"><span class="pre">RelaxedBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#RelaxedBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/relaxed_bernoulli.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_bernoulli.</span></span><span class="sig-name descname"><span class="pre">RelaxedBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#RelaxedBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a RelaxedBernoulli distribution, parametrized by
 <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.temperature" title="torch.distributions.relaxed_bernoulli.RelaxedBernoulli.temperature"><code class="xref py py-attr docutils literal notranslate"><span class="pre">temperature</span></code></a>, and either <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.probs" title="torch.distributions.relaxed_bernoulli.RelaxedBernoulli.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.logits" title="torch.distributions.relaxed_bernoulli.RelaxedBernoulli.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a>
@@ -3672,7 +3672,7 @@ <h2><span class="hidden-section">RelaxedBernoulli</span><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.RelaxedBernoulli.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#RelaxedBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/relaxed_bernoulli.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#RelaxedBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3707,7 +3707,7 @@ <h2><span class="hidden-section">RelaxedBernoulli</span><a class="headerlink" hr
 <h2><span class="hidden-section">LogitRelaxedBernoulli</span><a class="headerlink" href="#logitrelaxedbernoulli" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_bernoulli.</span></span><span class="sig-name descname"><span class="pre">LogitRelaxedBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/relaxed_bernoulli.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_bernoulli.</span></span><span class="sig-name descname"><span class="pre">LogitRelaxedBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a LogitRelaxedBernoulli distribution parameterized by <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.probs" title="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a>
 or <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.logits" title="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both), which is the logit of a RelaxedBernoulli
@@ -3733,12 +3733,12 @@ <h2><span class="hidden-section">LogitRelaxedBernoulli</span><a class="headerlin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/relaxed_bernoulli.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/relaxed_bernoulli.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3758,7 +3758,7 @@ <h2><span class="hidden-section">LogitRelaxedBernoulli</span><a class="headerlin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/relaxed_bernoulli.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3778,7 +3778,7 @@ <h2><span class="hidden-section">LogitRelaxedBernoulli</span><a class="headerlin
 <h2><span class="hidden-section">RelaxedOneHotCategorical</span><a class="headerlink" href="#relaxedonehotcategorical" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_categorical.</span></span><span class="sig-name descname"><span class="pre">RelaxedOneHotCategorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_categorical.html#RelaxedOneHotCategorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/relaxed_categorical.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_categorical.</span></span><span class="sig-name descname"><span class="pre">RelaxedOneHotCategorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_categorical.html#RelaxedOneHotCategorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_categorical.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a RelaxedOneHotCategorical distribution parametrized by
 <a class="reference internal" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.temperature" title="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.temperature"><code class="xref py py-attr docutils literal notranslate"><span class="pre">temperature</span></code></a>, and either <a class="reference internal" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.probs" title="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or <a class="reference internal" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.logits" title="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a>.
@@ -3807,7 +3807,7 @@ <h2><span class="hidden-section">RelaxedOneHotCategorical</span><a class="header
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_categorical.html#RelaxedOneHotCategorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/relaxed_categorical.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_categorical.html#RelaxedOneHotCategorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_categorical.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3842,7 +3842,7 @@ <h2><span class="hidden-section">RelaxedOneHotCategorical</span><a class="header
 <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#studentt" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.studentT.</span></span><span class="sig-name descname"><span class="pre">StudentT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/studentT.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.studentT.</span></span><span class="sig-name descname"><span class="pre">StudentT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Student’s t-distribution parameterized by degree of
 freedom <code class="xref py py-attr docutils literal notranslate"><span class="pre">df</span></code>, mean <code class="xref py py-attr docutils literal notranslate"><span class="pre">loc</span></code> and scale <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code>.</p>
@@ -3868,12 +3868,12 @@ <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#stu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/studentT.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/studentT.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3883,7 +3883,7 @@ <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#stu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/studentT.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3898,7 +3898,7 @@ <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#stu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/studentT.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3923,7 +3923,7 @@ <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#stu
 <h2><span class="hidden-section">TransformedDistribution</span><a class="headerlink" href="#transformeddistribution" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transformed_distribution.</span></span><span class="sig-name descname"><span class="pre">TransformedDistribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transformed_distribution.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transformed_distribution.</span></span><span class="sig-name descname"><span class="pre">TransformedDistribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Extension of the Distribution class, which applies a sequence of Transforms
 to a base distribution.  Let f be the composition of transforms applied:</p>
@@ -3961,14 +3961,14 @@ <h2><span class="hidden-section">TransformedDistribution</span><a class="headerl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transformed_distribution.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.cdf" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the cumulative distribution function by inverting the
 transform(s) and computing the score of the base distribution.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transformed_distribution.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3978,21 +3978,21 @@ <h2><span class="hidden-section">TransformedDistribution</span><a class="headerl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transformed_distribution.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.icdf" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the inverse cumulative distribution function using
 transform(s) and computing the score of the base distribution.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transformed_distribution.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scores the sample by inverting the transform(s) and computing the score
 using the score of the base distribution and the log abs det jacobian.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transformed_distribution.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a sample_shape shaped reparameterized sample or sample_shape
 shaped batch of reparameterized samples if the distribution parameters
 are batched. Samples first from base distribution and applies
@@ -4006,7 +4006,7 @@ <h2><span class="hidden-section">TransformedDistribution</span><a class="headerl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transformed_distribution.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.sample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a sample_shape shaped sample or sample_shape shaped batch of
 samples if the distribution parameters are batched. Samples first from
 base distribution and applies <cite>transform()</cite> for every transform in the
@@ -4030,7 +4030,7 @@ <h2><span class="hidden-section">TransformedDistribution</span><a class="headerl
 <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#uniform" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.uniform.</span></span><span class="sig-name descname"><span class="pre">Uniform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">low</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/uniform.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.uniform.</span></span><span class="sig-name descname"><span class="pre">Uniform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">low</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Generates uniformly distributed random samples from the half-open interval
 <code class="docutils literal notranslate"><span class="pre">[low,</span> <span class="pre">high)</span></code>.</p>
@@ -4055,17 +4055,17 @@ <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#unif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/uniform.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/uniform.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/uniform.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -4075,12 +4075,12 @@ <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#unif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/uniform.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/uniform.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -4095,7 +4095,7 @@ <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#unif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/uniform.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -4130,7 +4130,7 @@ <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#unif
 <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#vonmises" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.von_mises.VonMises">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.von_mises.</span></span><span class="sig-name descname"><span class="pre">VonMises</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/von_mises.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.von_mises.</span></span><span class="sig-name descname"><span class="pre">VonMises</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/von_mises.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>A circular von Mises distribution.</p>
 <p>This implementation uses polar coordinates. The <code class="docutils literal notranslate"><span class="pre">loc</span></code> and <code class="docutils literal notranslate"><span class="pre">value</span></code> args
@@ -4159,7 +4159,7 @@ <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#von
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.von_mises.VonMises.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/von_mises.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/von_mises.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -4169,7 +4169,7 @@ <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#von
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.von_mises.VonMises.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/von_mises.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/von_mises.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -4185,7 +4185,7 @@ <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#von
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.von_mises.VonMises.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/von_mises.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/von_mises.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.sample" title="Permalink to this definition">¶</a></dt>
 <dd><p>The sampling algorithm for the von Mises distribution is based on the
 following paper: D.J. Best and N.I. Fisher, “Efficient simulation of the
 von Mises distribution.” Applied Statistics (1979): 152-157.</p>
@@ -4212,7 +4212,7 @@ <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#von
 <h2><span class="hidden-section">Weibull</span><a class="headerlink" href="#weibull" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.weibull.Weibull">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.weibull.</span></span><span class="sig-name descname"><span class="pre">Weibull</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/weibull.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.weibull.</span></span><span class="sig-name descname"><span class="pre">Weibull</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/weibull.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Samples from a two-parameter Weibull distribution.</p>
 <p class="rubric">Example</p>
@@ -4236,12 +4236,12 @@ <h2><span class="hidden-section">Weibull</span><a class="headerlink" href="#weib
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.weibull.Weibull.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/weibull.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/weibull.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.weibull.Weibull.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/weibull.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/weibull.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -4271,7 +4271,7 @@ <h2><span class="hidden-section">Weibull</span><a class="headerlink" href="#weib
 <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wishart" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.wishart.</span></span><span class="sig-name descname"><span class="pre">Wishart</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_tril</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/wishart.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.wishart.</span></span><span class="sig-name descname"><span class="pre">Wishart</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_tril</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Wishart distribution parameterized by a symmetric positive definite matrix <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="normal">Σ</mi></mrow><annotation encoding="application/x-tex">\Sigma</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord">Σ</span></span></span></span></span>,
 or its Cholesky decomposition <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="bold">Σ</mi><mo>=</mo><mi mathvariant="bold">L</mi><msup><mi mathvariant="bold">L</mi><mi mathvariant="normal">⊤</mi></msup></mrow><annotation encoding="application/x-tex">\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6861em;"></span><span class="mord mathbf">Σ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8491em;"></span><span class="mord mathbf">L</span><span class="mord"><span class="mord mathbf">L</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8491em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">⊤</span></span></span></span></span></span></span></span></span></span></span></span></p>
@@ -4319,12 +4319,12 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/wishart.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/wishart.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -4334,7 +4334,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/wishart.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -4354,7 +4354,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_try_correction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/wishart.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_try_correction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>In some cases, sampling algorithm based on Bartlett decomposition may return singular matrix samples.
@@ -4392,7 +4392,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 <span id="kl-divergence"></span><h2><cite>KL Divergence</cite><a class="headerlink" href="#module-torch.distributions.kl" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributions.kl.kl_divergence">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.kl.</span></span><span class="sig-name descname"><span class="pre">kl_divergence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kl.html#kl_divergence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/kl.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kl.kl_divergence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.kl.</span></span><span class="sig-name descname"><span class="pre">kl_divergence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kl.html#kl_divergence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kl.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kl.kl_divergence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute Kullback-Leibler divergence <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>K</mi><mi>L</mi><mo stretchy="false">(</mo><mi>p</mi><mi mathvariant="normal">∥</mi><mi>q</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">KL(p \| q)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">K</span><span class="mord mathnormal">L</span><span class="mopen">(</span><span class="mord mathnormal">p</span><span class="mord">∥</span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mclose">)</span></span></span></span></span> between two distributions.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>K</mi><mi>L</mi><mo stretchy="false">(</mo><mi>p</mi><mi mathvariant="normal">∥</mi><mi>q</mi><mo stretchy="false">)</mo><mo>=</mo><mo>∫</mo><mi>p</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mi>log</mi><mo>⁡</mo><mfrac><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><mrow><mi>q</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac><mtext> </mtext><mi>d</mi><mi>x</mi></mrow><annotation encoding="application/x-tex">KL(p \| q) = \int p(x) \log\frac {p(x)} {q(x)} \,dx</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">K</span><span class="mord mathnormal">L</span><span class="mopen">(</span><span class="mord mathnormal">p</span><span class="mord">∥</span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:2.363em;vertical-align:-0.936em;"></span><span class="mop op-symbol large-op" style="margin-right:0.44445em;position:relative;top:-0.0011em;">∫</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop">lo<span style="margin-right:0.01389em;">g</span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.427em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.936em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">d</span><span class="mord mathnormal">x</span></span></span></span></span></div><dl class="field-list simple">
@@ -4509,7 +4509,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributions.kl.register_kl">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.kl.</span></span><span class="sig-name descname"><span class="pre">register_kl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type_p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_q</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kl.html#register_kl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/kl.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kl.register_kl" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.kl.</span></span><span class="sig-name descname"><span class="pre">register_kl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type_p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_q</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kl.html#register_kl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kl.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kl.register_kl" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decorator to register a pairwise function with <a class="reference internal" href="#torch.distributions.kl.kl_divergence" title="torch.distributions.kl.kl_divergence"><code class="xref py py-meth docutils literal notranslate"><span class="pre">kl_divergence()</span></code></a>.
 Usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@register_kl</span><span class="p">(</span><span class="n">Normal</span><span class="p">,</span> <span class="n">Normal</span><span class="p">)</span>
@@ -4545,13 +4545,13 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 <span id="transforms"></span><h2><cite>Transforms</cite><a class="headerlink" href="#module-torch.distributions.transforms" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.AbsTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">AbsTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#AbsTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L694"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.AbsTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">AbsTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#AbsTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L694"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.AbsTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi mathvariant="normal">∣</mi><mi>x</mi><mi mathvariant="normal">∣</mi></mrow><annotation encoding="application/x-tex">y = |x|</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">∣</span><span class="mord mathnormal">x</span><span class="mord">∣</span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.AffineTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">AffineTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#AffineTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.AffineTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">AffineTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#AffineTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.AffineTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the pointwise affine mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mtext>loc</mtext><mo>+</mo><mtext>scale</mtext><mo>×</mo><mi>x</mi></mrow><annotation encoding="application/x-tex">y = \text{loc} + \text{scale} \times x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord text"><span class="mord">loc</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord text"><span class="mord">scale</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -4568,7 +4568,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.CatTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CatTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tseq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CatTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L1024"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CatTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CatTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tseq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CatTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L1024"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CatTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform functor that applies a sequence of transforms <cite>tseq</cite>
 component-wise to each submatrix at <cite>dim</cite>, of length <cite>lengths[dim]</cite>,
 in a way compatible with <a class="reference internal" href="generated/torch.cat.html#torch.cat" title="torch.cat"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cat()</span></code></a>.</p>
@@ -4584,7 +4584,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.ComposeTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ComposeTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ComposeTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ComposeTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ComposeTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ComposeTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ComposeTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Composes multiple transforms in a chain.
 The transforms being composed are responsible for caching.</p>
 <dl class="field-list simple">
@@ -4600,7 +4600,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.CorrCholeskyTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CorrCholeskyTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CorrCholeskyTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CorrCholeskyTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CorrCholeskyTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CorrCholeskyTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CorrCholeskyTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transforms an uncontrained real vector <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> with length <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>D</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>D</mi><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mi mathvariant="normal">/</mi><mn>2</mn></mrow><annotation encoding="application/x-tex">D*(D-1)/2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">1</span><span class="mclose">)</span><span class="mord">/2</span></span></span></span></span> into the
 Cholesky factor of a D-dimension correlation matrix. This Cholesky factor is a lower
 triangular matrix with positive diagonals and unit Euclidean norm for each row.
@@ -4632,7 +4632,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.CumulativeDistributionTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CumulativeDistributionTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CumulativeDistributionTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L1202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CumulativeDistributionTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CumulativeDistributionTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CumulativeDistributionTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L1202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CumulativeDistributionTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the cumulative distribution function of a probability distribution.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -4654,13 +4654,13 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.ExpTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ExpTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ExpTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ExpTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ExpTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ExpTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ExpTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">y = \exp(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.IndependentTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">IndependentTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_transform</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reinterpreted_batch_ndims</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#IndependentTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.IndependentTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">IndependentTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_transform</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reinterpreted_batch_ndims</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#IndependentTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.IndependentTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around another transform to treat
 <code class="docutils literal notranslate"><span class="pre">reinterpreted_batch_ndims</span></code>-many extra of the right most dimensions as
 dependent. This has no effect on the forward or backward transforms, but
@@ -4679,7 +4679,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.LowerCholeskyTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">LowerCholeskyTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#LowerCholeskyTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L982"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.LowerCholeskyTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">LowerCholeskyTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#LowerCholeskyTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L982"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.LowerCholeskyTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform from unconstrained matrices to lower-triangular matrices with
 nonnegative diagonal entries.</p>
 <p>This is useful for parameterizing positive definite matrices in terms of
@@ -4688,19 +4688,19 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.PositiveDefiniteTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">PositiveDefiniteTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#PositiveDefiniteTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L1004"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.PositiveDefiniteTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">PositiveDefiniteTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#PositiveDefiniteTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L1004"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.PositiveDefiniteTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform from unconstrained matrices to positive-definite matrices.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.PowerTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">PowerTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exponent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#PowerTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.PowerTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">PowerTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exponent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#PowerTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.PowerTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><msup><mi>x</mi><mtext>exponent</mtext></msup></mrow><annotation encoding="application/x-tex">y = x^{\text{exponent}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7936em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7936em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">exponent</span></span></span></span></span></span></span></span></span></span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.ReshapeTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ReshapeTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ReshapeTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ReshapeTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ReshapeTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ReshapeTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ReshapeTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unit Jacobian transform to reshape the rightmost part of a tensor.</p>
 <p>Note that <code class="docutils literal notranslate"><span class="pre">in_shape</span></code> and <code class="docutils literal notranslate"><span class="pre">out_shape</span></code> must have the same number of
 elements, just as for <a class="reference internal" href="generated/torch.Tensor.reshape.html#torch.Tensor.reshape" title="torch.Tensor.reshape"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.reshape()</span></code></a>.</p>
@@ -4718,20 +4718,20 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.SigmoidTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SigmoidTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SigmoidTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SigmoidTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SigmoidTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SigmoidTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SigmoidTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">y = \frac{1}{1 + \exp(-x)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.3651em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span> and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mo>=</mo><mtext>logit</mtext><mo stretchy="false">(</mo><mi>y</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">x = \text{logit}(y)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">logit</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mclose">)</span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.SoftplusTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SoftplusTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SoftplusTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SoftplusTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SoftplusTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SoftplusTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SoftplusTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Softplus</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>log</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Softplus}(x) = \log(1 + \exp(x))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Softplus</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">lo<span style="margin-right:0.01389em;">g</span></span><span class="mopen">(</span><span class="mord">1</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">))</span></span></span></span></span>.
 The implementation reverts to the linear function when <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mo>&gt;</mo><mn>20</mn></mrow><annotation encoding="application/x-tex">x &gt; 20</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5782em;vertical-align:-0.0391em;"></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">&gt;</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">20</span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.TanhTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">TanhTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#TanhTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L658"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.TanhTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">TanhTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#TanhTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L658"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.TanhTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">y = \tanh(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">tanh</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span>.</p>
 <p>It is equivalent to
 <code class="docutils literal notranslate"><span class="pre">`</span>
@@ -4744,7 +4744,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.SoftmaxTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SoftmaxTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SoftmaxTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SoftmaxTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SoftmaxTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SoftmaxTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SoftmaxTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform from unconstrained space to the simplex via <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">y = \exp(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span> then
 normalizing.</p>
 <p>This is not bijective and cannot be used for HMC. However this acts mostly
@@ -4754,7 +4754,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.StackTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">StackTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tseq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#StackTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L1130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.StackTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">StackTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tseq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#StackTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L1130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.StackTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform functor that applies a sequence of transforms <cite>tseq</cite>
 component-wise to each submatrix at <cite>dim</cite>
 in a way compatible with <a class="reference internal" href="generated/torch.stack.html#torch.stack" title="torch.stack"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.stack()</span></code></a>.</p>
@@ -4768,7 +4768,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.StickBreakingTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">StickBreakingTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#StickBreakingTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L926"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.StickBreakingTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">StickBreakingTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#StickBreakingTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L926"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.StickBreakingTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform from unconstrained space to the simplex of one additional
 dimension via a stick-breaking process.</p>
 <p>This transform arises as an iterated sigmoid transform in a stick-breaking
@@ -4781,7 +4781,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.Transform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">Transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">Transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class for invertable transformations with computable log
 det jacobians. They are primarily used in
 <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.distributions.TransformedDistribution</span></code>.</p>
@@ -4840,20 +4840,20 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transforms.Transform.log_abs_det_jacobian">
-<span class="sig-name descname"><span class="pre">log_abs_det_jacobian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.log_abs_det_jacobian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.log_abs_det_jacobian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_abs_det_jacobian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.log_abs_det_jacobian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.log_abs_det_jacobian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the log det jacobian <cite>log |dy/dx|</cite> given input and output.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transforms.Transform.forward_shape">
-<span class="sig-name descname"><span class="pre">forward_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.forward_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.forward_shape" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.forward_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.forward_shape" title="Permalink to this definition">¶</a></dt>
 <dd><p>Infers the shape of the forward computation, given the input shape.
 Defaults to preserving shape.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transforms.Transform.inverse_shape">
-<span class="sig-name descname"><span class="pre">inverse_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.inverse_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/transforms.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.inverse_shape" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.inverse_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.inverse_shape" title="Permalink to this definition">¶</a></dt>
 <dd><p>Infers the shapes of the inverse computation, given the output shape.
 Defaults to preserving shape.</p>
 </dd></dl>
@@ -4865,7 +4865,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 <span id="constraints"></span><h2><cite>Constraints</cite><a class="headerlink" href="#module-torch.distributions.constraints" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.constraints.Constraint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">Constraint</span></span><a class="reference internal" href="_modules/torch/distributions/constraints.html#Constraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.Constraint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">Constraint</span></span><a class="reference internal" href="_modules/torch/distributions/constraints.html#Constraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.Constraint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract base class for constraints.</p>
 <p>A constraint object represents a region over which a variable is valid,
 e.g. within which a variable can be optimized.</p>
@@ -4882,7 +4882,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.constraints.Constraint.check">
-<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraints.html#Constraint.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.Constraint.check" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraints.html#Constraint.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.Constraint.check" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a byte tensor of <code class="docutils literal notranslate"><span class="pre">sample_shape</span> <span class="pre">+</span> <span class="pre">batch_shape</span></code> indicating
 whether each event in value satisfies this constraint.</p>
 </dd></dl>
@@ -4891,55 +4891,55 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.cat">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">cat</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.cat" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">cat</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.cat" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_Cat</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.dependent_property">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">dependent_property</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.dependent_property" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">dependent_property</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.dependent_property" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_DependentProperty</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.greater_than">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">greater_than</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.greater_than" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">greater_than</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.greater_than" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_GreaterThan</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.greater_than_eq">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">greater_than_eq</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.greater_than_eq" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">greater_than_eq</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.greater_than_eq" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_GreaterThanEq</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.independent">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">independent</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.independent" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">independent</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.independent" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_IndependentConstraint</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.integer_interval">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">integer_interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.integer_interval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">integer_interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.integer_interval" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_IntegerInterval</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.interval">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.interval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.interval" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_Interval</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.half_open_interval">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">half_open_interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.half_open_interval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">half_open_interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.half_open_interval" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_HalfOpenInterval</span></code></p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributions.constraints.is_dependent">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">is_dependent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constraint</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraints.html#is_dependent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.is_dependent" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">is_dependent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constraint</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraints.html#is_dependent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.is_dependent" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checks if <code class="docutils literal notranslate"><span class="pre">constraint</span></code> is a <code class="docutils literal notranslate"><span class="pre">_Dependent</span></code> object.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -4972,19 +4972,19 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.less_than">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">less_than</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.less_than" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">less_than</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.less_than" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_LessThan</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.multinomial">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">multinomial</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.multinomial" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">multinomial</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.multinomial" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_Multinomial</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.stack">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">stack</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraints.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.stack" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">stack</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.stack" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_Stack</span></code></p>
 </dd></dl>
 
@@ -5057,11 +5057,11 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 object.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.constraint_registry.ConstraintRegistry">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.constraint_registry.</span></span><span class="sig-name descname"><span class="pre">ConstraintRegistry</span></span><a class="reference internal" href="_modules/torch/distributions/constraint_registry.html#ConstraintRegistry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraint_registry.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraint_registry.ConstraintRegistry" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.constraint_registry.</span></span><span class="sig-name descname"><span class="pre">ConstraintRegistry</span></span><a class="reference internal" href="_modules/torch/distributions/constraint_registry.html#ConstraintRegistry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraint_registry.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraint_registry.ConstraintRegistry" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registry to link constraints to transforms.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.constraint_registry.ConstraintRegistry.register">
-<span class="sig-name descname"><span class="pre">register</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constraint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraint_registry.html#ConstraintRegistry.register"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributions/constraint_registry.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraint_registry.ConstraintRegistry.register" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constraint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraint_registry.html#ConstraintRegistry.register"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraint_registry.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraint_registry.ConstraintRegistry.register" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a <a class="reference internal" href="#torch.distributions.constraints.Constraint" title="torch.distributions.constraints.Constraint"><code class="xref py py-class docutils literal notranslate"><span class="pre">Constraint</span></code></a>
 subclass in this registry. Usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@my_registry</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">MyConstraintClass</span><span class="p">)</span>
diff --git a/main/dlpack.html b/main/dlpack.html
index df3956ac00c..53778d07629 100644
--- a/main/dlpack.html
+++ b/main/dlpack.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <h1>torch.utils.dlpack<a class="headerlink" href="#torch-utils-dlpack" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.dlpack.from_dlpack">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.dlpack.</span></span><span class="sig-name descname"><span class="pre">from_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ext_tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="_modules/torch/utils/dlpack.html#from_dlpack"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/dlpack.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.dlpack.from_dlpack" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.dlpack.</span></span><span class="sig-name descname"><span class="pre">from_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ext_tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="_modules/torch/utils/dlpack.html#from_dlpack"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/dlpack.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.dlpack.from_dlpack" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts a tensor from an external library into a <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>.</p>
 <p>The returned PyTorch tensor will share the memory with the input tensor
 (which may have come from another library). Note that in-place operations
diff --git a/main/elastic/agent.html b/main/elastic/agent.html
index cd7d47ba1d6..de984573108 100644
--- a/main/elastic/agent.html
+++ b/main/elastic/agent.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -638,7 +638,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 are relevant to understanding the role of the <code class="docutils literal notranslate"><span class="pre">agent</span></code> in torchelastic.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.ElasticAgent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">ElasticAgent</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">ElasticAgent</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent" title="Permalink to this definition">¶</a></dt>
 <dd><p>An agent process responsible for managing one or more worker processes.</p>
 <p>The worker processes are assumed to be regular distributed PyTorch scripts.
 When the worker process is created by the agent, the agent provides the
@@ -670,7 +670,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.ElasticAgent.get_worker_group">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_worker_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.get_worker_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.get_worker_group" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_worker_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.get_worker_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.get_worker_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the <code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code> for the given <code class="docutils literal notranslate"><span class="pre">role</span></code>.</p>
 <p>Note that the worker group is a mutable object and hence in a
 multi-threaded/process environment it may change state.
@@ -685,7 +685,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.ElasticAgent.run">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.run" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run the agent.</p>
 <p>Supports retrying the worker group on failures up to <code class="docutils literal notranslate"><span class="pre">max_restarts</span></code>.</p>
 <dl class="field-list simple">
@@ -706,7 +706,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerSpec">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rdzv_handler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_restarts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_port</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rdzv_handler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_restarts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_port</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec" title="Permalink to this definition">¶</a></dt>
 <dd><p>Blueprint information about a particular type of worker.</p>
 <p>For a given role, there must only exist a single worker spec.
 Worker spec is expected to be homogeneous across all nodes (machine),
@@ -737,7 +737,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerSpec.get_entrypoint_name">
-<span class="sig-name descname"><span class="pre">get_entrypoint_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerSpec.get_entrypoint_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec.get_entrypoint_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_entrypoint_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerSpec.get_entrypoint_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec.get_entrypoint_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the entry point name.</p>
 <p>If the entrypoint is a function (e.g. <code class="docutils literal notranslate"><span class="pre">Callable</span></code>) returns its <code class="docutils literal notranslate"><span class="pre">__qualname__</span></code>
 else if the entrypoint is a binary (e.g. <code class="docutils literal notranslate"><span class="pre">str</span></code>), returns the binary name.</p>
@@ -747,7 +747,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerState">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState" title="Permalink to this definition">¶</a></dt>
 <dd><p>A state of the <code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code>.</p>
 <p>Workers in a worker group change state as a unit. If a single worker
 in a worker group fails the entire set is considered failed:</p>
@@ -779,7 +779,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 self terminating and allowing the job manager to retry the node.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerState.is_running">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_running</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState.is_running"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState.is_running" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_running</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState.is_running"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState.is_running" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the Worker.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -796,7 +796,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.Worker">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">Worker</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#Worker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.Worker" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">Worker</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#Worker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.Worker" title="Permalink to this definition">¶</a></dt>
 <dd><p>A worker instance.</p>
 <p>Contrast this with <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code> that represents the specifications of a
 worker. A <code class="docutils literal notranslate"><span class="pre">Worker</span></code> is created from a <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code>. A <code class="docutils literal notranslate"><span class="pre">Worker</span></code> is to
@@ -821,7 +821,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerGroup">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerGroup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerGroup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerGroup" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerGroup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerGroup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerGroup" title="Permalink to this definition">¶</a></dt>
 <dd><p>A set of <code class="docutils literal notranslate"><span class="pre">Worker</span></code> instances.</p>
 <p>The class defines a set of <code class="docutils literal notranslate"><span class="pre">Worker</span></code> instances for the given <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code> managed by <code class="docutils literal notranslate"><span class="pre">ElasticAgent</span></code>. Whether the worker
 group contains cross instance workers or not depends on the implementation of the agent.</p>
@@ -835,7 +835,7 @@ <h2>Implementations<a class="headerlink" href="#implementations" title="Permalin
 <p>Below are the agent implementations provided by torchelastic.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.local_elastic_agent.</span></span><span class="sig-name descname"><span class="pre">LocalElasticAgent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefix_template</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/local_elastic_agent.html#LocalElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/local_elastic_agent.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.local_elastic_agent.</span></span><span class="sig-name descname"><span class="pre">LocalElasticAgent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefix_template</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/local_elastic_agent.html#LocalElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/local_elastic_agent.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent" title="Permalink to this definition">¶</a></dt>
 <dd><p>An implementation of <code class="xref py py-class docutils literal notranslate"><span class="pre">torchelastic.agent.server.ElasticAgent</span></code> that handles host-local workers.</p>
 <p>This agent is deployed per host and is configured to spawn <code class="docutils literal notranslate"><span class="pre">n</span></code> workers.
 When using GPUs, <code class="docutils literal notranslate"><span class="pre">n</span></code> maps to the number of GPUs available on the host.</p>
@@ -921,7 +921,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 to implement.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">SimpleElasticAgent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">SimpleElasticAgent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent" title="Permalink to this definition">¶</a></dt>
 <dd><p>An <code class="docutils literal notranslate"><span class="pre">ElasticAgent</span></code> that manages one particular type of worker role.</p>
 <p>An <code class="docutils literal notranslate"><span class="pre">ElasticAgent</span></code> that manages workers (<code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code>) for a single <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code>
 such as one particular type of worker role.</p>
@@ -929,7 +929,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._assign_worker_ranks">
-<span class="sig-name descname"><span class="pre">_assign_worker_ranks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._assign_worker_ranks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._assign_worker_ranks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_assign_worker_ranks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._assign_worker_ranks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._assign_worker_ranks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine proper ranks for worker processes.</p>
 <p>Fast Path: when all workers have the same role and world size. We calculate
 the global rank to be group_rank * group_world_size + local_rank. And the
@@ -965,7 +965,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._exit_barrier">
-<span class="sig-name descname"><span class="pre">_exit_barrier</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._exit_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L923"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._exit_barrier" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_exit_barrier</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._exit_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L923"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._exit_barrier" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a barrier that keeps the agent process alive until all workers finish.</p>
 <p>Wait for <code class="docutils literal notranslate"><span class="pre">exit_barrier_timeout</span></code> seconds for all agents to finish
 executing their local workers (either successfully or not). This
@@ -975,7 +975,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._initialize_workers">
-<span class="sig-name descname"><span class="pre">_initialize_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._initialize_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._initialize_workers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_initialize_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._initialize_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._initialize_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Start a fresh set of workers for the worker_group.</p>
 <p>Essentially, a rendezvous followed by a <code class="docutils literal notranslate"><span class="pre">start_workers</span></code>.
 The caller should first call <code class="docutils literal notranslate"><span class="pre">_stop_workers()</span></code> to stop running workers
@@ -989,7 +989,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._monitor_workers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_monitor_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._monitor_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._monitor_workers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_monitor_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._monitor_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._monitor_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check on the workers for the <code class="docutils literal notranslate"><span class="pre">worker_group</span></code>.</p>
 <p>This function also returns the new state of the worker group.</p>
 <dl class="field-list simple">
@@ -1001,7 +1001,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._rendezvous">
-<span class="sig-name descname"><span class="pre">_rendezvous</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._rendezvous"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._rendezvous" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_rendezvous</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._rendezvous"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._rendezvous" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run rendezvous for the workers specified by the worker spec.</p>
 <p>Assigns workers a new global rank and world size.
 Updates the rendezvous store for the worker group.</p>
@@ -1011,7 +1011,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._restart_workers">
-<span class="sig-name descname"><span class="pre">_restart_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._restart_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._restart_workers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_restart_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._restart_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._restart_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Restart (stops, rendezvous, starts) all local workers in the group.</p>
 <dl class="field-list simple">
 </dl>
@@ -1019,7 +1019,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._shutdown">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_shutdown</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">death_sig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Signals.SIGTERM</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_restart</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._shutdown" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_shutdown</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">death_sig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Signals.SIGTERM</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_restart</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._shutdown" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clean up any resources that were allocated during the agent’s work.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1030,7 +1030,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._start_workers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_start_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._start_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._start_workers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_start_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._start_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._start_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Start <code class="docutils literal notranslate"><span class="pre">worker_group.spec.local_world_size</span></code> number of workers.</p>
 <p>This is according to worker spec for the worker group .
 Returns a map of <code class="docutils literal notranslate"><span class="pre">local_rank</span></code> to worker <code class="docutils literal notranslate"><span class="pre">id</span></code>.</p>
@@ -1043,7 +1043,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._stop_workers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_stop_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_restart</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._stop_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._stop_workers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_stop_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_restart</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._stop_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._stop_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Stop all workers in the given worker group.</p>
 <p>Implementors must deal with workers in all states defined by
 <code class="docutils literal notranslate"><span class="pre">WorkerState</span></code>. That is, it must gracefully handle stopping
@@ -1056,7 +1056,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.api.RunResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.api.</span></span><span class="sig-name descname"><span class="pre">RunResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_values=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#RunResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/api.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.api.RunResult" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.api.</span></span><span class="sig-name descname"><span class="pre">RunResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_values=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#RunResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.api.RunResult" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return results of the worker executions.</p>
 <p>Run results follow an “all-or-nothing” policy where the run is successful if and
 only if ALL local workers managed by this agent complete successfully.</p>
@@ -1105,7 +1105,7 @@ <h2>Health Check Server<a class="headerlink" href="#health-check-server" title="
 Additionally, health check server will have callback to check watchdog is alive.</p>
 <span class="target" id="module-torch.distributed.elastic.agent.server.health_check_server"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.health_check_server.</span></span><span class="sig-name descname"><span class="pre">HealthCheckServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alive_callback</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">port</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/health_check_server.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.health_check_server.</span></span><span class="sig-name descname"><span class="pre">HealthCheckServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alive_callback</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">port</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/health_check_server.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Interface for health check monitoring server, which can be extended
 by starting tcp/http server on the specified port.</p>
 <dl class="field-list simple">
@@ -1119,7 +1119,7 @@ <h2>Health Check Server<a class="headerlink" href="#health-check-server" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.start">
-<span class="sig-name descname"><span class="pre">start</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer.start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/health_check_server.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.start" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">start</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer.start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/health_check_server.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.start" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unsupported functionality for Pytorch, doesn’t start any health check server</p>
 <dl class="field-list simple">
 </dl>
@@ -1127,7 +1127,7 @@ <h2>Health Check Server<a class="headerlink" href="#health-check-server" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.stop">
-<span class="sig-name descname"><span class="pre">stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer.stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/health_check_server.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.stop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer.stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/health_check_server.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.stop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to stop health check server</p>
 <dl class="field-list simple">
 </dl>
@@ -1137,7 +1137,7 @@ <h2>Health Check Server<a class="headerlink" href="#health-check-server" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.health_check_server.create_healthcheck_server">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.health_check_server.</span></span><span class="sig-name descname"><span class="pre">create_healthcheck_server</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alive_callback</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">port</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#create_healthcheck_server"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/agent/server/health_check_server.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.create_healthcheck_server" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.health_check_server.</span></span><span class="sig-name descname"><span class="pre">create_healthcheck_server</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alive_callback</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">port</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#create_healthcheck_server"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/health_check_server.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.create_healthcheck_server" title="Permalink to this definition">¶</a></dt>
 <dd><p>creates health check server object</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/elastic/control_plane.html b/main/elastic/control_plane.html
index fd380cc7bcd..bfd837d4f04 100644
--- a/main/elastic/control_plane.html
+++ b/main/elastic/control_plane.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 into your application.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.control_plane.worker_main">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.control_plane.</span></span><span class="sig-name descname"><span class="pre">worker_main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/control_plane.html#worker_main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/control_plane.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.control_plane.worker_main" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.control_plane.</span></span><span class="sig-name descname"><span class="pre">worker_main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/control_plane.html#worker_main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/control_plane.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.control_plane.worker_main" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a context manager that wraps your main entry function. This combines
 the existing <code class="docutils literal notranslate"><span class="pre">errors.record</span></code> logic as well as a new <code class="docutils literal notranslate"><span class="pre">_WorkerServer</span></code> that
 exposes handlers via a unix socket specified by
diff --git a/main/elastic/customization.html b/main/elastic/customization.html
index d2762efe707..ef0d79015bf 100644
--- a/main/elastic/customization.html
+++ b/main/elastic/customization.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/errors.html b/main/elastic/errors.html
index 17fa118e052..e6102b1802a 100644
--- a/main/elastic/errors.html
+++ b/main/elastic/errors.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -661,7 +661,7 @@
 <h2>Methods and Classes<a class="headerlink" href="#methods-and-classes" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.record">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_handler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/errors/__init__.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_handler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/errors/__init__.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.record" title="Permalink to this definition">¶</a></dt>
 <dd><p>Syntactic sugar to record errors/exceptions that happened in the decorated
 function using the provided <code class="docutils literal notranslate"><span class="pre">error_handler</span></code>.</p>
 <p>Using this decorator is equivalent to:</p>
@@ -701,7 +701,7 @@ <h2>Methods and Classes<a class="headerlink" href="#methods-and-classes" title="
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.ChildFailedError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ChildFailedError</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ChildFailedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/errors/__init__.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ChildFailedError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ChildFailedError</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ChildFailedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/errors/__init__.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ChildFailedError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Special exception type that can be raised from a function annotated with the
 <code class="docutils literal notranslate"><span class="pre">&#64;record</span></code> decorator to have the child process’ (root exception) propagate
 up the stack as-is (e.g. without being wrapped in the parent’s traceback).</p>
@@ -738,7 +738,7 @@ <h2>Methods and Classes<a class="headerlink" href="#methods-and-classes" title="
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.ErrorHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ErrorHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors/error_handler.html#ErrorHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/errors/error_handler.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ErrorHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ErrorHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors/error_handler.html#ErrorHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/errors/error_handler.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ErrorHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write the provided exception object along with some other metadata about
 the error in a structured way in JSON format to an error file specified by the
 environment variable: <code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_ERROR_FILE</span></code>. If this environment
@@ -750,7 +750,7 @@ <h2>Methods and Classes<a class="headerlink" href="#methods-and-classes" title="
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.ProcessFailure">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ProcessFailure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exitcode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_file</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ProcessFailure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/errors/__init__.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ProcessFailure" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ProcessFailure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exitcode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_file</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ProcessFailure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/errors/__init__.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ProcessFailure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent the failed process result. When the worker process fails, it may record failure root cause into the file.</p>
 <p>Tries to read the failure timestamp from the provided <code class="docutils literal notranslate"><span class="pre">error_file</span></code>,
 if the <code class="docutils literal notranslate"><span class="pre">error_file</span></code> does not exist, the timestamp is the current
diff --git a/main/elastic/events.html b/main/elastic/events.html
index b46a803d9ce..3b0a6434786 100644
--- a/main/elastic/events.html
+++ b/main/elastic/events.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -621,14 +621,14 @@
 <h2>API Methods<a class="headerlink" href="#api-methods" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.record">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'null'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events.html#record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/events/__init__.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'null'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events.html#record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/__init__.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.record" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.construct_and_record_rdzv_event">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">construct_and_record_rdzv_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hostname</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_endpoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events.html#construct_and_record_rdzv_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/events/__init__.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.construct_and_record_rdzv_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">construct_and_record_rdzv_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hostname</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_endpoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events.html#construct_and_record_rdzv_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/__init__.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.construct_and_record_rdzv_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize rendezvous event object and record its operations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -675,7 +675,7 @@ <h2>API Methods<a class="headerlink" href="#api-methods" title="Permalink to thi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.get_logging_handler">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">get_logging_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'null'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/handlers.html#get_logging_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/events/handlers.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.get_logging_handler" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">get_logging_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'null'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/handlers.html#get_logging_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/handlers.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.get_logging_handler" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.Handler" title="(in Python v3.13)"><em>Handler</em></a></p>
@@ -688,7 +688,7 @@ <h2>API Methods<a class="headerlink" href="#api-methods" title="Permalink to thi
 <h2>Event Objects<a class="headerlink" href="#event-objects" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.api.Event">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.api.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timestamp=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/api.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/events/api.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.api.Event" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.api.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timestamp=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/api.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/api.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.api.Event" title="Permalink to this definition">¶</a></dt>
 <dd><p>The class represents the generic event that occurs during the torchelastic job execution.</p>
 <p>The event can be any kind of meaningful action.</p>
 <dl class="field-list simple">
@@ -705,7 +705,7 @@ <h2>Event Objects<a class="headerlink" href="#event-objects" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.api.EventSource">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.api.</span></span><span class="sig-name descname"><span class="pre">EventSource</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/api.html#EventSource"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/events/api.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.api.EventSource" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.api.</span></span><span class="sig-name descname"><span class="pre">EventSource</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/api.html#EventSource"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/api.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.api.EventSource" title="Permalink to this definition">¶</a></dt>
 <dd><p>Known identifiers of the event producers.</p>
 </dd></dl>
 
diff --git a/main/elastic/examples.html b/main/elastic/examples.html
index 389ad2bd113..77a1e59baba 100644
--- a/main/elastic/examples.html
+++ b/main/elastic/examples.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/kubernetes.html b/main/elastic/kubernetes.html
index 344a0cdd5d6..99df6864687 100644
--- a/main/elastic/kubernetes.html
+++ b/main/elastic/kubernetes.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/metrics.html b/main/elastic/metrics.html
index b0b9cb6dbc9..d475751ba9b 100644
--- a/main/elastic/metrics.html
+++ b/main/elastic/metrics.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -721,17 +721,17 @@ <h2>Metric Handlers<a class="headerlink" href="#metric-handlers" title="Permalin
 <p>Below are the metric handlers that come included with torchelastic.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.api.MetricHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">MetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#MetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/metrics/api.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.MetricHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">MetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#MetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.MetricHandler" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.api.ConsoleMetricHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">ConsoleMetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#ConsoleMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/metrics/api.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.ConsoleMetricHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">ConsoleMetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#ConsoleMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.ConsoleMetricHandler" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.api.NullMetricHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">NullMetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#NullMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/metrics/api.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.NullMetricHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">NullMetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#NullMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.NullMetricHandler" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
@@ -739,14 +739,14 @@ <h2>Metric Handlers<a class="headerlink" href="#metric-handlers" title="Permalin
 <h2>Methods<a class="headerlink" href="#methods" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.configure">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">configure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">handler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#configure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/metrics/api.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.configure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">configure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">handler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#configure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.configure" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.prof">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">prof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#prof"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/metrics/api.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.prof" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">prof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#prof"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.prof" title="Permalink to this definition">¶</a></dt>
 <dd><p>&#64;profile decorator publishes duration.ms, count, success, failure metrics for the function that it decorates.</p>
 <p>The metric name defaults to the qualified name (<code class="docutils literal notranslate"><span class="pre">class_name.def_name</span></code>) of the function.
 If the function does not belong to a class, it uses the leaf module name instead.</p>
@@ -766,7 +766,7 @@ <h2>Methods<a class="headerlink" href="#methods" title="Permalink to this headin
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.put_metric">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">put_metric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metric_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#put_metric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/metrics/api.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.put_metric" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">put_metric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metric_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#put_metric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.put_metric" title="Permalink to this definition">¶</a></dt>
 <dd><p>Publish a metric data point.</p>
 <p>Usage</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">put_metric</span><span class="p">(</span><span class="s2">&quot;metric_name&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
diff --git a/main/elastic/multiprocessing.html b/main/elastic/multiprocessing.html
index 04480f2252c..fda620f8d34 100644
--- a/main/elastic/multiprocessing.html
+++ b/main/elastic/multiprocessing.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -665,7 +665,7 @@
 <h2>Starting Multiple Workers<a class="headerlink" href="#starting-multiple-workers" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.start_processes">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">start_processes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing.html#start_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/__init__.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.start_processes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">start_processes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing.html#start_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/__init__.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.start_processes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Start <code class="docutils literal notranslate"><span class="pre">n</span></code> copies of <code class="docutils literal notranslate"><span class="pre">entrypoint</span></code> processes with the provided options.</p>
 <p><code class="docutils literal notranslate"><span class="pre">entrypoint</span></code> is either a <code class="docutils literal notranslate"><span class="pre">Callable</span></code> (function) or a <code class="docutils literal notranslate"><span class="pre">str</span></code> (binary).
 The number of copies is determined by the number of entries for <code class="docutils literal notranslate"><span class="pre">args</span></code> and
@@ -769,7 +769,7 @@ <h2>Starting Multiple Workers<a class="headerlink" href="#starting-multiple-work
 <h2>Process Context<a class="headerlink" href="#process-context" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.PContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">PContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#PContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/api.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.PContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">PContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#PContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.PContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>The base class that standardizes operations over a set of processes that are launched via different mechanisms.</p>
 <p>The name <code class="docutils literal notranslate"><span class="pre">PContext</span></code> is intentional to disambiguate with <code class="docutils literal notranslate"><span class="pre">torch.multiprocessing.ProcessContext</span></code>.</p>
 <div class="admonition warning">
@@ -784,7 +784,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.MultiprocessContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">MultiprocessContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#MultiprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/api.py#L617"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.MultiprocessContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">MultiprocessContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#MultiprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L617"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.MultiprocessContext" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">PContext</span></code> holding worker processes invoked as a function.</p>
 <dl class="field-list simple">
 </dl>
@@ -792,7 +792,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.SubprocessContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">SubprocessContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#SubprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/api.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.SubprocessContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">SubprocessContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#SubprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.SubprocessContext" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">PContext</span></code> holding worker processes invoked as a binary.</p>
 <dl class="field-list simple">
 </dl>
@@ -800,7 +800,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.RunProcsResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">RunProcsResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_values=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#RunProcsResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/api.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.RunProcsResult" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">RunProcsResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_values=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#RunProcsResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.RunProcsResult" title="Permalink to this definition">¶</a></dt>
 <dd><p>Results of a completed run of processes started with <code class="docutils literal notranslate"><span class="pre">start_processes()</span></code>. Returned by <code class="docutils literal notranslate"><span class="pre">PContext</span></code>.</p>
 <p>Note the following:</p>
 <ol class="arabic simple">
@@ -815,7 +815,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">DefaultLogsSpecs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redirects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_ranks_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#DefaultLogsSpecs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/api.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">DefaultLogsSpecs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redirects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_ranks_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#DefaultLogsSpecs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Default LogsSpecs implementation:</p>
 <ul class="simple">
 <li><p><cite>log_dir</cite> will be created if it doesn’t exist</p></li>
@@ -825,7 +825,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs.reify">
-<span class="sig-name descname"><span class="pre">reify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">envs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#DefaultLogsSpecs.reify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/api.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs.reify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">envs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#DefaultLogsSpecs.reify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs.reify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Uses following scheme to build log destination paths:</p>
 <ul class="simple">
 <li><p><cite>&lt;log_dir&gt;/&lt;rdzv_run_id&gt;/attempt_&lt;attempt&gt;/&lt;rank&gt;/stdout.log</cite></p></li>
@@ -843,7 +843,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.LogsDest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">LogsDest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stderrs=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_files=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsDest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/api.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsDest" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">LogsDest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stderrs=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_files=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsDest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsDest" title="Permalink to this definition">¶</a></dt>
 <dd><p>For each log type, holds mapping of local rank ids to file paths.</p>
 <dl class="field-list simple">
 </dl>
@@ -851,7 +851,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.LogsSpecs">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">LogsSpecs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redirects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_ranks_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsSpecs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/api.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsSpecs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">LogsSpecs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redirects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_ranks_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsSpecs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsSpecs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Defines logs processing and redirection for each worker process.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -868,7 +868,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.LogsSpecs.reify">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">envs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsSpecs.reify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/api.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsSpecs.reify" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">envs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsSpecs.reify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsSpecs.reify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given the environment variables, builds destination of log files for each of the local ranks.</p>
 <p>Envs parameter contains env variables dict for each of the local ranks, where entries are defined in:
 <code class="xref py py-func docutils literal notranslate"><span class="pre">_start_workers()</span></code>.</p>
diff --git a/main/elastic/quickstart.html b/main/elastic/quickstart.html
index e3fa3b8e743..8c37311734e 100644
--- a/main/elastic/quickstart.html
+++ b/main/elastic/quickstart.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/rendezvous.html b/main/elastic/rendezvous.html
index 8907b5a0421..e172d25c38f 100644
--- a/main/elastic/rendezvous.html
+++ b/main/elastic/rendezvous.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -713,7 +713,7 @@
 <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousParameters">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">endpoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">endpoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Hold the parameters to construct a <a class="reference internal" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="torch.distributed.elastic.rendezvous.RendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RendezvousHandler</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -730,7 +730,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousParameters.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the value for <code class="docutils literal notranslate"><span class="pre">key</span></code> if <code class="docutils literal notranslate"><span class="pre">key</span></code> exists, else <code class="docutils literal notranslate"><span class="pre">default</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -741,7 +741,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_bool">
-<span class="sig-name descname"><span class="pre">get_as_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get_as_bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_as_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get_as_bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_bool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the value for <code class="docutils literal notranslate"><span class="pre">key</span></code> as a <code class="docutils literal notranslate"><span class="pre">bool</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -752,7 +752,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_int">
-<span class="sig-name descname"><span class="pre">get_as_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get_as_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_as_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get_as_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the value for <code class="docutils literal notranslate"><span class="pre">key</span></code> as an <code class="docutils literal notranslate"><span class="pre">int</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -765,7 +765,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandlerRegistry">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousHandlerRegistry</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandlerRegistry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandlerRegistry" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousHandlerRegistry</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandlerRegistry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandlerRegistry" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent a registry of <a class="reference internal" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="torch.distributed.elastic.rendezvous.RendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RendezvousHandler</span></code></a> backends.</p>
 <dl class="field-list simple">
 </dl>
@@ -776,7 +776,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Main rendezvous interface.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -786,7 +786,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.get_backend">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.get_backend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.get_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the name of the rendezvous backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -797,7 +797,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.get_run_id">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_run_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.get_run_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.get_run_id" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_run_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.get_run_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.get_run_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the run id of the rendezvous.</p>
 <p>The run id is a user-defined id that uniquely identifies an instance of
 a distributed application. It typically maps to a job id and is used to
@@ -811,7 +811,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.is_closed">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_closed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.is_closed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.is_closed" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_closed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.is_closed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.is_closed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check whether the rendezvous has been closed.</p>
 <p>A closed rendezvous means all future attempts to re-rendezvous within
 same job will fail.</p>
@@ -829,7 +829,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.next_rendezvous">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">next_rendezvous</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.next_rendezvous"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.next_rendezvous" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">next_rendezvous</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.next_rendezvous"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.next_rendezvous" title="Permalink to this definition">¶</a></dt>
 <dd><p>Main entry-point into the rendezvous barrier.</p>
 <p>Blocks until the rendezvous is complete and the current process is
 included in the formed worker group, or a timeout occurs, or the
@@ -854,7 +854,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.num_nodes_waiting">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">num_nodes_waiting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.num_nodes_waiting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.num_nodes_waiting" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">num_nodes_waiting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.num_nodes_waiting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.num_nodes_waiting" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of nodes who arrived late at the rendezvous
 barrier, hence were not included in the current worker group.</p>
 <p>Callers should periodically call this method to check whether new
@@ -869,13 +869,13 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.set_closed">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_closed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.set_closed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.set_closed" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_closed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.set_closed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.set_closed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark the rendezvous as closed.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.shutdown">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shutdown</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.shutdown" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shutdown</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.shutdown" title="Permalink to this definition">¶</a></dt>
 <dd><p>Close all resources that were open for the rendezvous.</p>
 <p>Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">rdzv_handler</span> <span class="o">=</span> <span class="o">...</span>
@@ -908,7 +908,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 <h2>Dataclasses<a class="headerlink" href="#dataclasses" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousInfo">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_store_info</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousInfo" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_store_info</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousInfo" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds the information about the rendezvous.</p>
 <dl class="field-list simple">
 </dl>
@@ -916,13 +916,13 @@ <h2>Dataclasses<a class="headerlink" href="#dataclasses" title="Permalink to thi
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousStoreInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">master_addr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_port</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStoreInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousStoreInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">master_addr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_port</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStoreInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo" title="Permalink to this definition">¶</a></dt>
 <dd><p>Store address and port that can be used to bootstrap trainer distributed comms</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo.build">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStoreInfo.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo.build" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStoreInfo.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo.build" title="Permalink to this definition">¶</a></dt>
 <dd><p>Factory method, finds unused new port on rank0 host and addr/port info with all ranks.</p>
 <p>If master_addr/master_port is knowns (useful when sharing existing tcp store server) use the constructor.</p>
 <dl class="field-list simple">
@@ -947,37 +947,37 @@ <h2>Dataclasses<a class="headerlink" href="#dataclasses" title="Permalink to thi
 <h2>Exceptions<a class="headerlink" href="#exceptions" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents the base type for rendezvous errors.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousClosedError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousClosedError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousClosedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousClosedError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousClosedError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousClosedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousClosedError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when a rendezvous is closed.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousTimeoutError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousTimeoutError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousTimeoutError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousTimeoutError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousTimeoutError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousTimeoutError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousTimeoutError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when a rendezvous did not complete on time.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousConnectionError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousConnectionError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousConnectionError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousConnectionError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousConnectionError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousConnectionError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousConnectionError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when the connection to a rendezvous backend has failed.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousStateError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousStateError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStateError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStateError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousStateError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStateError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStateError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when the state of a rendezvous is corrupt.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousGracefulExitError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousGracefulExitError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousGracefulExitError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/api.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousGracefulExitError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousGracefulExitError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousGracefulExitError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousGracefulExitError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when node wasn’t not included in rendezvous and gracefully exits.</p>
 <p>Exception is a mechanism to exit the stack, however does not mean a failure.</p>
 </dd></dl>
@@ -989,7 +989,7 @@ <h2>Implementations<a class="headerlink" href="#implementations" title="Permalin
 <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Permalink to this heading">¶</a></h3>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.create_handler">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">create_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#create_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L1380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.create_handler" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">create_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#create_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L1380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.create_handler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new <a class="reference internal" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler" title="torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">DynamicRendezvousHandler</span></code></a> from the specified parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1032,13 +1032,13 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">DynamicRendezvousHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#DynamicRendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">DynamicRendezvousHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#DynamicRendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent a handler that sets up a rendezvous among a set of nodes.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler.from_backend">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#DynamicRendezvousHandler.from_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L1010"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler.from_backend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#DynamicRendezvousHandler.from_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L1010"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler.from_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new <a class="reference internal" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler" title="torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">DynamicRendezvousHandler</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1059,11 +1059,11 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousBackend</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousBackend</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent a backend that holds the rendezvous state.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.get_state">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the rendezvous state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1090,7 +1090,7 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.set_state">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the rendezvous state.</p>
 <p>The new rendezvous state is set conditionally:</p>
 <blockquote>
@@ -1135,7 +1135,7 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousTimeout">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousTimeout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">join</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">close</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heartbeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousTimeout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousTimeout" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousTimeout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">join</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">close</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heartbeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousTimeout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousTimeout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Hold the timeout configuration of a rendezvous.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1181,7 +1181,7 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to this heading">¶</a></h4>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.create_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">create_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#create_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.create_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">create_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#create_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.create_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new <a class="reference internal" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend" title="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend"><code class="xref py py-class docutils literal notranslate"><span class="pre">C10dRendezvousBackend</span></code></a> from the specified parameters.</p>
 <table class="docutils colwidths-auto align-default">
 <thead>
@@ -1231,7 +1231,7 @@ <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">C10dRendezvousBackend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">C10dRendezvousBackend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents a C10d-backed rendezvous backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1244,7 +1244,7 @@ <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.get_state">
-<span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>See base class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1261,7 +1261,7 @@ <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.set_state">
-<span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>See base class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1277,7 +1277,7 @@ <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to t
 <h4>Etcd Backend<a class="headerlink" href="#etcd-backend" title="Permalink to this heading">¶</a></h4>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.create_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">create_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#create_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.create_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">create_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#create_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.create_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new <a class="reference internal" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend" title="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend"><code class="xref py py-class docutils literal notranslate"><span class="pre">EtcdRendezvousBackend</span></code></a> from the specified parameters.</p>
 <table class="docutils colwidths-auto align-default">
 <thead>
@@ -1317,7 +1317,7 @@ <h4>Etcd Backend<a class="headerlink" href="#etcd-backend" title="Permalink to t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">EtcdRendezvousBackend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ttl</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">EtcdRendezvousBackend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ttl</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents an etcd-based rendezvous backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1331,7 +1331,7 @@ <h4>Etcd Backend<a class="headerlink" href="#etcd-backend" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.get_state">
-<span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>See base class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1348,7 +1348,7 @@ <h4>Etcd Backend<a class="headerlink" href="#etcd-backend" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.set_state">
-<span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>See base class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1371,7 +1371,7 @@ <h3>Etcd Rendezvous (Legacy)<a class="headerlink" href="#etcd-rendezvous-legacy"
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous.EtcdRendezvousHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous.</span></span><span class="sig-name descname"><span class="pre">EtcdRendezvousHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rdzv_impl</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous.html#EtcdRendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_rendezvous.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous.EtcdRendezvousHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous.</span></span><span class="sig-name descname"><span class="pre">EtcdRendezvousHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rdzv_impl</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous.html#EtcdRendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous.EtcdRendezvousHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements a
 <a class="reference internal" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="torch.distributed.elastic.rendezvous.RendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.distributed.elastic.rendezvous.RendezvousHandler</span></code></a> interface
 backed by
@@ -1452,14 +1452,14 @@ <h3>Etcd Store<a class="headerlink" href="#etcd-store" title="Permalink to this
 <code class="docutils literal notranslate"><span class="pre">next_rendezvous()</span></code> when etcd is used as the rendezvous backend.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_store.</span></span><span class="sig-name descname"><span class="pre">EtcdStore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">etcd_client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">etcd_store_prefix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_store.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_store.</span></span><span class="sig-name descname"><span class="pre">EtcdStore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">etcd_client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">etcd_store_prefix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement a c10 Store interface by piggybacking on the rendezvous etcd instance.</p>
 <p>This is the store object returned by <code class="docutils literal notranslate"><span class="pre">EtcdRendezvous</span></code>.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.add">
-<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_store.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.add" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.add" title="Permalink to this definition">¶</a></dt>
 <dd><p>Atomically increment a value by an integer amount.</p>
 <p>The integer is represented as a string using base 10. If key is not present,
 a default value of <code class="docutils literal notranslate"><span class="pre">0</span></code> will be assumed.</p>
@@ -1475,7 +1475,7 @@ <h3>Etcd Store<a class="headerlink" href="#etcd-store" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.check">
-<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_store.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.check" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.check" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all of the keys are immediately present (without waiting).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1486,7 +1486,7 @@ <h3>Etcd Store<a class="headerlink" href="#etcd-store" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_store.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get a value by key, possibly doing a blocking wait.</p>
 <p>If key is not immediately present, will do a blocking wait
 for at most <code class="docutils literal notranslate"><span class="pre">timeout</span></code> duration or until the key is published.</p>
@@ -1505,14 +1505,14 @@ <h3>Etcd Store<a class="headerlink" href="#etcd-store" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.set">
-<span class="sig-name descname"><span class="pre">set</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.set"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_store.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.set" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.set"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.set" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write a key/value pair into <code class="docutils literal notranslate"><span class="pre">EtcdStore</span></code>.</p>
 <p>Both key and value may be either Python <code class="docutils literal notranslate"><span class="pre">str</span></code> or <code class="docutils literal notranslate"><span class="pre">bytes</span></code>.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">override_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_store.py#L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">override_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait until all of the keys are published, or until timeout.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Raises</dt>
@@ -1538,7 +1538,7 @@ <h3>Etcd Server<a class="headerlink" href="#etcd-server" title="Permalink to thi
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_server.EtcdServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_server.</span></span><span class="sig-name descname"><span class="pre">EtcdServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_server.html#EtcdServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/rendezvous/etcd_server.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_server.EtcdServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_server.</span></span><span class="sig-name descname"><span class="pre">EtcdServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_server.html#EtcdServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_server.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_server.EtcdServer" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>tested on etcd server v3.4.3.</p>
diff --git a/main/elastic/run.html b/main/elastic/run.html
index 245d37834c8..18e5c5c0dde 100644
--- a/main/elastic/run.html
+++ b/main/elastic/run.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/subprocess_handler.html b/main/elastic/subprocess_handler.html
index 2ccb6d6af40..78c575a6e87 100644
--- a/main/elastic/subprocess_handler.html
+++ b/main/elastic/subprocess_handler.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h2>Retrieve SubprocessHandler<a class="headerlink" href="#retrieve-subprocesshandler" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.get_subprocess_handler">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.</span></span><span class="sig-name descname"><span class="pre">get_subprocess_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.html#get_subprocess_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.get_subprocess_handler" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.</span></span><span class="sig-name descname"><span class="pre">get_subprocess_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.html#get_subprocess_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.get_subprocess_handler" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler" title="torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler"><em>SubprocessHandler</em></a></p>
@@ -627,7 +627,7 @@ <h2>Retrieve SubprocessHandler<a class="headerlink" href="#retrieve-subprocessha
 <h2>SubprocessHandler<a class="headerlink" href="#subprocesshandler" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.</span></span><span class="sig-name descname"><span class="pre">SubprocessHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.html#SubprocessHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.</span></span><span class="sig-name descname"><span class="pre">SubprocessHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.html#SubprocessHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convenience wrapper around python’s <code class="docutils literal notranslate"><span class="pre">subprocess.Popen</span></code>. Keeps track of
 meta-objects associated to the process (e.g. stdout and stderr redirect fds).</p>
 <dl class="field-list simple">
diff --git a/main/elastic/timer.html b/main/elastic/timer.html
index b98e5ee5271..6b36fa1b61a 100644
--- a/main/elastic/timer.html
+++ b/main/elastic/timer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -645,7 +645,7 @@
 <h2>Client Methods<a class="headerlink" href="#client-methods" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.configure">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">configure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timer_client</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#configure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.configure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">configure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timer_client</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#configure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.configure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Configures a timer client. Must be called before using <code class="docutils literal notranslate"><span class="pre">expires</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -653,7 +653,7 @@ <h2>Client Methods<a class="headerlink" href="#client-methods" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.expires">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">expires</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">after</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">client</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#expires"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.expires" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">expires</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">after</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">client</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#expires"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.expires" title="Permalink to this definition">¶</a></dt>
 <dd><p>Acquires a countdown timer that expires in <code class="docutils literal notranslate"><span class="pre">after</span></code> seconds from now,
 unless the code-block that it wraps is finished within the timeframe.
 When the timer expires, this worker is eligible to be reaped. The
@@ -687,7 +687,7 @@ <h2>Server/Client Implementations<a class="headerlink" href="#server-client-impl
 a <code class="docutils literal notranslate"><span class="pre">multiprocess.Queue</span></code>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.LocalTimerServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">LocalTimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mp_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">60</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/local_timer.html#LocalTimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/local_timer.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.LocalTimerServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">LocalTimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mp_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">60</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/local_timer.html#LocalTimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/local_timer.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.LocalTimerServer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Server that works with <code class="docutils literal notranslate"><span class="pre">LocalTimerClient</span></code>. Clients are expected to be
 subprocesses to the parent process that is running this server. Each host
 in the job is expected to start its own timer server locally and each
@@ -699,7 +699,7 @@ <h2>Server/Client Implementations<a class="headerlink" href="#server-client-impl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.LocalTimerClient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">LocalTimerClient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mp_queue</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/local_timer.html#LocalTimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/local_timer.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.LocalTimerClient" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">LocalTimerClient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mp_queue</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/local_timer.html#LocalTimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/local_timer.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.LocalTimerClient" title="Permalink to this definition">¶</a></dt>
 <dd><p>Client side of <code class="docutils literal notranslate"><span class="pre">LocalTimerServer</span></code>. This client is meant to be used
 on the same host that the <code class="docutils literal notranslate"><span class="pre">LocalTimerServer</span></code> is running on and uses
 pid to uniquely identify a worker. This is particularly useful in situations
@@ -711,7 +711,7 @@ <h2>Server/Client Implementations<a class="headerlink" href="#server-client-impl
 based on a named pipe.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.FileTimerServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">FileTimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/file_based_local_timer.html#FileTimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/file_based_local_timer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.FileTimerServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">FileTimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/file_based_local_timer.html#FileTimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/file_based_local_timer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.FileTimerServer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Server that works with <code class="docutils literal notranslate"><span class="pre">FileTimerClient</span></code>. Clients are expected to be
 running on the same host as the process that is running this server.
 Each host in the job is expected to start its own timer server locally
@@ -733,7 +733,7 @@ <h2>Server/Client Implementations<a class="headerlink" href="#server-client-impl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.FileTimerClient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">FileTimerClient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">signal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Signals.SIGKILL</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/file_based_local_timer.html#FileTimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/file_based_local_timer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.FileTimerClient" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">FileTimerClient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">signal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Signals.SIGKILL</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/file_based_local_timer.html#FileTimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/file_based_local_timer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.FileTimerClient" title="Permalink to this definition">¶</a></dt>
 <dd><p>Client side of <code class="docutils literal notranslate"><span class="pre">FileTimerServer</span></code>. This client is meant to be used
 on the same host that the <code class="docutils literal notranslate"><span class="pre">FileTimerServer</span></code> is running on and uses
 pid to uniquely identify a worker.
@@ -763,7 +763,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 the server and client.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerRequest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerRequest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expiration_time</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerRequest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerRequest" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerRequest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expiration_time</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerRequest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerRequest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Data object representing a countdown timer acquisition and release
 that is used between the <code class="docutils literal notranslate"><span class="pre">TimerClient</span></code> and <code class="docutils literal notranslate"><span class="pre">TimerServer</span></code>.
 A negative <code class="docutils literal notranslate"><span class="pre">expiration_time</span></code> should be interpreted as a “release”
@@ -780,7 +780,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">request_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">request_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Entity that monitors active timers and expires them
 in a timely fashion. This server is responsible for
 reaping workers that have expired timers.</p>
@@ -788,7 +788,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerServer.clear_timers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">clear_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_ids</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.clear_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.clear_timers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">clear_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_ids</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.clear_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.clear_timers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clears all timers for the given <code class="docutils literal notranslate"><span class="pre">worker_ids</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -796,7 +796,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerServer.get_expired_timers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_expired_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deadline</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.get_expired_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.get_expired_timers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_expired_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deadline</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.get_expired_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.get_expired_timers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns all expired timers for each worker_id. An expired timer
 is a timer for which the expiration_time is less than or equal to
 the provided deadline.</p>
@@ -809,7 +809,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerServer.register_timers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">register_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timer_requests</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.register_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.register_timers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">register_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timer_requests</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.register_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.register_timers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Processes the incoming timer requests and registers them with the server.
 The timer request can either be a acquire-timer or release-timer request.
 Timer requests with a negative expiration_time should be interpreted
@@ -822,12 +822,12 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerClient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerClient</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerClient</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient" title="Permalink to this definition">¶</a></dt>
 <dd><p>Client library to acquire and release countdown timers by communicating
 with the TimerServer.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerClient.acquire">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">acquire</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expiration_time</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient.acquire"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient.acquire" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">acquire</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expiration_time</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient.acquire"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient.acquire" title="Permalink to this definition">¶</a></dt>
 <dd><p>Acquires a timer for the worker that holds this client object
 given the scope_id and expiration_time. Typically registers
 the timer with the TimerServer.</p>
@@ -837,7 +837,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerClient.release">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">release</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient.release"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/api.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient.release" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">release</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient.release"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient.release" title="Permalink to this definition">¶</a></dt>
 <dd><p>Releases the timer for the <code class="docutils literal notranslate"><span class="pre">scope_id</span></code> on the worker this
 client represents. After this method is
 called, the countdown timer on the scope is no longer in effect.</p>
@@ -852,7 +852,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 <span id="debug-info-logging"></span><h2>Debug info logging<a class="headerlink" href="#module-torch.distributed.elastic.timer.debug_info_logging" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.debug_info_logging.log_debug_info_for_expired_timers">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.debug_info_logging.</span></span><span class="sig-name descname"><span class="pre">log_debug_info_for_expired_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expired_timers</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/debug_info_logging.html#log_debug_info_for_expired_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/elastic/timer/debug_info_logging.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.debug_info_logging.log_debug_info_for_expired_timers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.debug_info_logging.</span></span><span class="sig-name descname"><span class="pre">log_debug_info_for_expired_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expired_timers</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/debug_info_logging.html#log_debug_info_for_expired_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/debug_info_logging.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.debug_info_logging.log_debug_info_for_expired_timers" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
diff --git a/main/elastic/train_script.html b/main/elastic/train_script.html
index 4a5ad025521..fd802ceffd6 100644
--- a/main/elastic/train_script.html
+++ b/main/elastic/train_script.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/export.html b/main/export.html
index a43da294673..8e471cac2a1 100644
--- a/main/export.html
+++ b/main/export.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1593,7 +1593,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 <span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#module-torch.export" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.export">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_module_call_signature</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/__init__.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_module_call_signature</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/__init__.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.export" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.export.export" title="torch.export.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">export()</span></code></a> takes any nn.Module along with example inputs, and produces a traced graph representing
 only the Tensor computation of the function in an Ahead-of-Time (AOT) fashion,
 which can subsequently be executed with different inputs or serialized.  The
@@ -1677,7 +1677,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.save">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/__init__.py#L382"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/__init__.py#L382"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.save" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>Under active development, saved files may not be usable in newer versions
@@ -1724,7 +1724,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.load">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_opset_version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/__init__.py#L466"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_opset_version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/__init__.py#L466"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.load" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>Under active development, saved files may not be usable in newer versions
@@ -1775,7 +1775,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.register_dataclass">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">register_dataclass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">serialized_type_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#register_dataclass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/__init__.py#L579"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.register_dataclass" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">register_dataclass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">serialized_type_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#register_dataclass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/__init__.py#L579"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.register_dataclass" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a dataclass as a valid input/output type for <a class="reference internal" href="#torch.export.export" title="torch.export.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.export.export()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1816,7 +1816,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.dynamic_shapes.Dim">
-<span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">Dim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#Dim"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/dynamic_shapes.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.Dim" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">Dim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#Dim"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.Dim" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.export.dynamic_shapes.Dim" title="torch.export.dynamic_shapes.Dim"><code class="xref py py-func docutils literal notranslate"><span class="pre">Dim()</span></code></a> constructs a type analogous to a named symbolic integer with a range.
 It can be used to describe multiple possible values of a dynamic tensor dimension.
 Note that different dynamic dimensions of the same tensor, or of different tensors,
@@ -1837,7 +1837,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.exported_program.default_decompositions">
-<span class="sig-prename descclassname"><span class="pre">torch.export.exported_program.</span></span><span class="sig-name descname"><span class="pre">default_decompositions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#default_decompositions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.exported_program.default_decompositions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.exported_program.</span></span><span class="sig-name descname"><span class="pre">default_decompositions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#default_decompositions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.exported_program.default_decompositions" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the default decomposition table which contains decomposition of
 all ATEN operators to core aten opset. Use this API together with
 <code class="xref py py-func docutils literal notranslate"><span class="pre">run_decompositions()</span></code></p>
@@ -1850,7 +1850,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.dims">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">dims</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#dims"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/dynamic_shapes.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dims" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">dims</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#dims"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dims" title="Permalink to this definition">¶</a></dt>
 <dd><p>Util to create multiple <code class="xref py py-func docutils literal notranslate"><span class="pre">Dim()</span></code> types.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1864,7 +1864,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.dynamic_shapes.ShapesCollection">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapesCollection</span></span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#ShapesCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/dynamic_shapes.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.ShapesCollection" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapesCollection</span></span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#ShapesCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.ShapesCollection" title="Permalink to this definition">¶</a></dt>
 <dd><p>Builder for dynamic_shapes.
 Used to assign dynamic shape specifications to tensors that appear in inputs.</p>
 <p>This is useful particularly when <code class="xref py py-func docutils literal notranslate"><span class="pre">args()</span></code> is a nested input structure, and it’s
@@ -1885,7 +1885,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes">
-<span class="sig-name descname"><span class="pre">dynamic_shapes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#ShapesCollection.dynamic_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/dynamic_shapes.py#L651"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">dynamic_shapes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#ShapesCollection.dynamic_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L651"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates the <a class="reference internal" href="#torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes" title="torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes"><code class="xref py py-func docutils literal notranslate"><span class="pre">dynamic_shapes()</span></code></a> pytree structure according to <code class="xref py py-func docutils literal notranslate"><span class="pre">args()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">kwargs()</span></code>.</p>
 </dd></dl>
 
@@ -1893,7 +1893,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.dynamic_shapes.refine_dynamic_shapes_from_suggested_fixes">
-<span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">refine_dynamic_shapes_from_suggested_fixes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#refine_dynamic_shapes_from_suggested_fixes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/dynamic_shapes.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.refine_dynamic_shapes_from_suggested_fixes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">refine_dynamic_shapes_from_suggested_fixes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#refine_dynamic_shapes_from_suggested_fixes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.refine_dynamic_shapes_from_suggested_fixes" title="Permalink to this definition">¶</a></dt>
 <dd><p>When exporting with <code class="xref py py-func docutils literal notranslate"><span class="pre">dynamic_shapes()</span></code>, export may fail with a ConstraintViolation error if the specification
 doesn’t match the constraints inferred from tracing the model. The error message may provide suggested fixes -
 changes that can be made to <code class="xref py py-func docutils literal notranslate"><span class="pre">dynamic_shapes()</span></code> to export successfully.</p>
@@ -1932,7 +1932,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportedProgram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_signature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_constraints</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_call_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constants</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verifiers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportedProgram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_signature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_constraints</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_call_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constants</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verifiers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram" title="Permalink to this definition">¶</a></dt>
 <dd><p>Package of a program from <a class="reference internal" href="#torch.export.export" title="torch.export.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">export()</span></code></a>. It contains
 an <a class="reference internal" href="fx.html#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.fx.Graph</span></code></a> that represents Tensor computation, a state_dict containing
 tensor values of all lifted parameters and buffers, and various metadata.</p>
@@ -1947,7 +1947,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.module">
-<span class="sig-name descname"><span class="pre">module</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">module</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a self contained GraphModule with all the parameters/buffers inlined.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1958,7 +1958,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.buffers">
-<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an iterator over original module buffers.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1973,7 +1973,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an iterator over original module buffers, yielding
 both the name of the buffer as well as the buffer itself.</p>
 <div class="admonition warning">
@@ -1989,7 +1989,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.parameters">
-<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L958"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L958"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an iterator over original module’s parameters.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -2004,7 +2004,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L966"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L966"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an iterator over original module parameters, yielding
 both the name of the parameter as well as the parameter itself.</p>
 <div class="admonition warning">
@@ -2020,7 +2020,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.run_decompositions">
-<span class="sig-name descname"><span class="pre">run_decompositions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decomp_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decompose_custom_triton_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.run_decompositions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.run_decompositions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_decompositions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decomp_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decompose_custom_triton_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.run_decompositions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.run_decompositions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run a set of decompositions on the exported program and returns a new
 exported program. By default we will run the Core ATen decompositions to
 get operators in the
@@ -2055,14 +2055,14 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ExportBackwardSignature">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportBackwardSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradients_to_parameters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradients_to_user_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportBackwardSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportBackwardSignature" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportBackwardSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradients_to_parameters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradients_to_user_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportBackwardSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportBackwardSignature" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ExportGraphSignature">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportGraphSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportGraphSignature" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportGraphSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportGraphSignature" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.export.ExportGraphSignature" title="torch.export.ExportGraphSignature"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExportGraphSignature</span></code></a> models the input/output signature of Export Graph,
 which is a fx.Graph with stronger invariants gurantees.</p>
 <p>Export Graph is functional and does not access “states” like parameters
@@ -2135,21 +2135,21 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ModuleCallSignature">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ModuleCallSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_spec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils._pytree.TreeSpec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_spec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils._pytree.TreeSpec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_arg_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ModuleCallSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ModuleCallSignature" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ModuleCallSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_spec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils._pytree.TreeSpec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_spec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils._pytree.TreeSpec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_arg_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ModuleCallSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ModuleCallSignature" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ModuleCallEntry">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ModuleCallEntry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fqn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">signature</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.export.ModuleCallSignature" title="torch.export.exported_program.ModuleCallSignature"><span class="pre">torch.export.exported_program.ModuleCallSignature</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ModuleCallEntry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/exported_program.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ModuleCallEntry" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ModuleCallEntry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fqn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">signature</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.export.ModuleCallSignature" title="torch.export.exported_program.ModuleCallSignature"><span class="pre">torch.export.exported_program.ModuleCallSignature</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ModuleCallEntry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ModuleCallEntry" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <span class="target" id="module-torch.export.decomp_utils"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.decomp_utils.</span></span><span class="sig-name descname"><span class="pre">CustomDecompTable</span></span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/decomp_utils.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.decomp_utils.</span></span><span class="sig-name descname"><span class="pre">CustomDecompTable</span></span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a custom dictionary that is specifically used for handling decomp_table in export.
 The reason we need this is because in the new world, you can only <em>delete</em> an op from decomp
 table to preserve it. This is problematic for custom ops because we don’t know when the custom
@@ -2167,7 +2167,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.copy">
-<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/decomp_utils.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.copy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.copy" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="#torch.export.decomp_utils.CustomDecompTable" title="torch.export.decomp_utils.CustomDecompTable"><em>CustomDecompTable</em></a></p>
@@ -2177,17 +2177,17 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.items">
-<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/decomp_utils.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.items" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.items" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.keys">
-<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/decomp_utils.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.keys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.keys" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.materialize">
-<span class="sig-name descname"><span class="pre">materialize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.materialize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/decomp_utils.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.materialize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">materialize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.materialize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.materialize" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[torch._ops.OperatorBase, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><em>Callable</em></a>]</p>
@@ -2197,66 +2197,66 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.pop">
-<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/decomp_utils.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.pop" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.update">
-<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/decomp_utils.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.update" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <span class="target" id="module-torch.export.exported_program"></span><span class="target" id="module-torch.export.graph_signature"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.InputKind">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">InputKind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#InputKind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.InputKind" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">InputKind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#InputKind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.InputKind" title="Permalink to this definition">¶</a></dt>
 <dd><p>An enumeration.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.InputSpec">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">InputSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.export.graph_signature.InputKind" title="torch.export.graph_signature.InputKind"><span class="pre">torch.export.graph_signature.InputKind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#InputSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.InputSpec" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">InputSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.export.graph_signature.InputKind" title="torch.export.graph_signature.InputKind"><span class="pre">torch.export.graph_signature.InputKind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#InputSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.InputSpec" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.OutputKind">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">OutputKind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#OutputKind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.OutputKind" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">OutputKind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#OutputKind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.OutputKind" title="Permalink to this definition">¶</a></dt>
 <dd><p>An enumeration.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.OutputSpec">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">OutputSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.export.graph_signature.OutputKind" title="torch.export.graph_signature.OutputKind"><span class="pre">torch.export.graph_signature.OutputKind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#OutputSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.OutputSpec" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">OutputSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.export.graph_signature.OutputKind" title="torch.export.graph_signature.OutputKind"><span class="pre">torch.export.graph_signature.OutputKind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#OutputSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.OutputSpec" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.SymIntArgument">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymIntArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymIntArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymIntArgument" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymIntArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymIntArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymIntArgument" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.SymBoolArgument">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymBoolArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymBoolArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymBoolArgument" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymBoolArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymBoolArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymBoolArgument" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.SymFloatArgument">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymFloatArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymFloatArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymFloatArgument" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymFloatArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymFloatArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymFloatArgument" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.ExportGraphSignature">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">ExportGraphSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">ExportGraphSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.export.graph_signature.ExportGraphSignature" title="torch.export.graph_signature.ExportGraphSignature"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExportGraphSignature</span></code></a> models the input/output signature of Export Graph,
 which is a fx.Graph with stronger invariants gurantees.</p>
 <p>Export Graph is functional and does not access “states” like parameters
@@ -2327,7 +2327,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.graph_signature.ExportGraphSignature.replace_all_uses">
-<span class="sig-name descname"><span class="pre">replace_all_uses</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">old</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature.replace_all_uses"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature.replace_all_uses" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replace_all_uses</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">old</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature.replace_all_uses"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature.replace_all_uses" title="Permalink to this definition">¶</a></dt>
 <dd><p>Replace all uses of the old name with new name in the signature.</p>
 <dl class="field-list simple">
 </dl>
@@ -2335,25 +2335,25 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.graph_signature.ExportGraphSignature.get_replace_hook">
-<span class="sig-name descname"><span class="pre">get_replace_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">replace_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature.get_replace_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature.get_replace_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_replace_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">replace_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature.get_replace_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature.get_replace_hook" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.CustomObjArgument">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">CustomObjArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_fqn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fake_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch._library.fake_class_registry.FakeScriptObject</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#CustomObjArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/graph_signature.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.CustomObjArgument" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">CustomObjArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_fqn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fake_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch._library.fake_class_registry.FakeScriptObject</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#CustomObjArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.CustomObjArgument" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <span class="target" id="module-torch.export.dynamic_shapes"></span><span class="target" id="module-torch.export.unflatten"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.export.unflatten.FlatArgsAdapter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">FlatArgsAdapter</span></span><a class="reference internal" href="_modules/torch/export/unflatten.html#FlatArgsAdapter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/unflatten.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.FlatArgsAdapter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">FlatArgsAdapter</span></span><a class="reference internal" href="_modules/torch/export/unflatten.html#FlatArgsAdapter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.FlatArgsAdapter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adapts input arguments with <code class="docutils literal notranslate"><span class="pre">input_spec</span></code> to align <code class="docutils literal notranslate"><span class="pre">target_spec</span></code>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.unflatten.FlatArgsAdapter.adapt">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">adapt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#FlatArgsAdapter.adapt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/unflatten.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.FlatArgsAdapter.adapt" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">adapt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#FlatArgsAdapter.adapt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.FlatArgsAdapter.adapt" title="Permalink to this definition">¶</a></dt>
 <dd><p>NOTE: This adapter may mutate given <code class="docutils literal notranslate"><span class="pre">input_args_with_path</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2366,7 +2366,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.unflatten.InterpreterModule">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">InterpreterModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#InterpreterModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/unflatten.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.InterpreterModule" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">InterpreterModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#InterpreterModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.InterpreterModule" title="Permalink to this definition">¶</a></dt>
 <dd><p>A module that uses torch.fx.Interpreter to execute instead of the usual
 codegen that GraphModule uses. This provides better stack trace information
 and makes it easier to debug execution.</p>
@@ -2376,7 +2376,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.unflatten.InterpreterModuleDispatcher">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">InterpreterModuleDispatcher</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">call_modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#InterpreterModuleDispatcher"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/unflatten.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.InterpreterModuleDispatcher" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">InterpreterModuleDispatcher</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">call_modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#InterpreterModuleDispatcher"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.InterpreterModuleDispatcher" title="Permalink to this definition">¶</a></dt>
 <dd><p>A module that carries a sequence of InterpreterModules corresponding to
 a sequence of calls of that module. Each call to the module dispatches
 to the next InterpreterModule, and wraps back around after the last.</p>
@@ -2386,7 +2386,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.unflatten.unflatten">
-<span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat_args_adapter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/unflatten.py#L682"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.unflatten" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat_args_adapter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L682"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.unflatten" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unflatten an ExportedProgram, producing a module with the same module
 hierarchy as the original eager module. This can be useful if you are trying
 to use <a class="reference internal" href="#module-torch.export" title="torch.export"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.export</span></code></a> with another system that expects a module
@@ -2418,7 +2418,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <span class="target" id="module-torch.export.custom_obj"></span><span class="target" id="module-torch.export.experimental"></span><span class="target" id="module-torch.export.passes"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.export.passes.move_to_device_pass">
-<span class="sig-prename descclassname"><span class="pre">torch.export.passes.</span></span><span class="sig-name descname"><span class="pre">move_to_device_pass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/passes.html#move_to_device_pass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/export/passes/__init__.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.passes.move_to_device_pass" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.passes.</span></span><span class="sig-name descname"><span class="pre">move_to_device_pass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/passes.html#move_to_device_pass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/passes/__init__.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.passes.move_to_device_pass" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move the exported program to the given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/export.ir_spec.html b/main/export.ir_spec.html
index 4a92be1c09d..4fc88a8ecd4 100644
--- a/main/export.ir_spec.html
+++ b/main/export.ir_spec.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/export.programming_model.html b/main/export.programming_model.html
index 43babdcec39..7cb3c2465ec 100644
--- a/main/export.programming_model.html
+++ b/main/export.programming_model.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/fft.html b/main/fft.html
index fbdf62112ba..fd765c57c83 100644
--- a/main/fft.html
+++ b/main/fft.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/fsdp.html b/main/fsdp.html
index 753cc33dfc8..3702e56faea 100644
--- a/main/fsdp.html
+++ b/main/fsdp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="fullyshardeddataparallel"></span><h1>FullyShardedDataParallel<a class="headerlink" href="#module-torch.distributed.fsdp" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullyShardedDataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sharding_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_offload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_wrap_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward_prefetch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">BackwardPrefetch.BACKWARD_PRE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignored_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_init_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync_module_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_prefetch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit_all_gathers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_orig_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignored_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullyShardedDataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sharding_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_offload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_wrap_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward_prefetch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">BackwardPrefetch.BACKWARD_PRE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignored_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_init_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync_module_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_prefetch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit_all_gathers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_orig_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignored_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>A wrapper for sharding module parameters across data parallel workers.</p>
 <p>This is inspired by <a class="reference external" href="https://arxiv.org/abs/2004.13336">Xu et al.</a> as well as the ZeRO Stage 3 from <a class="reference external" href="https://www.deepspeed.ai/">DeepSpeed</a>.
 FullyShardedDataParallel is commonly shortened to FSDP.</p>
@@ -868,7 +868,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L575"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L575"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p>
 <p>Typical use includes initializing the parameters of a model (see also <a class="reference internal" href="nn.init.html#nn-init-doc"><span class="std std-ref">torch.nn.init</span></a>).</p>
 <p>Compared to <code class="docutils literal notranslate"><span class="pre">torch.nn.Module.apply</span></code>, this version additionally gathers
@@ -889,7 +889,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.check_is_root">
-<span class="sig-name descname"><span class="pre">check_is_root</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.check_is_root"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.check_is_root" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">check_is_root</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.check_is_root"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.check_is_root" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if this instance is a root FSDP module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -900,7 +900,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_">
-<span class="sig-name descname"><span class="pre">clip_grad_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.clip_grad_norm_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clip_grad_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.clip_grad_norm_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clip the gradient norm of all parameters.</p>
 <p>The norm is computed over all parameters’ gradients as viewed as a single vector, and the
 gradients are modified in-place.</p>
@@ -941,7 +941,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.flatten_sharded_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">flatten_sharded_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sharded_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.flatten_sharded_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.flatten_sharded_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">flatten_sharded_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sharded_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.flatten_sharded_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.flatten_sharded_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Flatten a sharded optimizer state-dict.</p>
 <p>The API is similar to <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">shard_full_optim_state_dict()</span></code></a>. The only
 difference is that the input <code class="docutils literal notranslate"><span class="pre">sharded_optim_state_dict</span></code> should be
@@ -969,7 +969,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run the forward pass for the wrapped module, inserting FSDP-specific pre- and post-forward sharding logic.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -980,7 +980,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.fsdp_modules">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fsdp_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.fsdp_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L552"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.fsdp_modules" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fsdp_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.fsdp_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L552"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.fsdp_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all nested FSDP instances.</p>
 <p>This possibly includes <code class="docutils literal notranslate"><span class="pre">module</span></code> itself and only includes FSDP root modules if <code class="docutils literal notranslate"><span class="pre">root_only=True</span></code>.</p>
 <dl class="field-list simple">
@@ -1004,7 +1004,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the full optimizer state-dict.</p>
 <p>Consolidates the full optimizer state on rank 0 and returns it
 as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a> following the convention of
@@ -1057,7 +1057,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.get_state_dict_type">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.get_state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L751"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.get_state_dict_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.get_state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L751"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.get_state_dict_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the state_dict_type and the corresponding configurations for the FSDP modules rooted at <code class="docutils literal notranslate"><span class="pre">module</span></code>.</p>
 <p>The target module does not have to be an FSDP module.</p>
 <dl class="field-list simple">
@@ -1085,7 +1085,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers, yielding both the name of the buffer and the buffer itself.</p>
 <p>Intercepts buffer names and removes all occurrences of the FSDP-specific flattened buffer prefix
 when inside the <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params" title="torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">summon_full_params()</span></code></a> context manager.</p>
@@ -1098,7 +1098,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L989"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L989"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters, yielding both the name of the parameter and the parameter itself.</p>
 <p>Intercepts parameter names and removes all occurrences of the FSDP-specific flattened parameter prefix
 when inside the <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params" title="torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">summon_full_params()</span></code></a> context manager.</p>
@@ -1111,7 +1111,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.no_sync">
-<span class="sig-name descname"><span class="pre">no_sync</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.no_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.no_sync" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">no_sync</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.no_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.no_sync" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable gradient synchronizations across FSDP instances.</p>
 <p>Within this context, gradients will be accumulated in module
 variables, which will later be synchronized in the first
@@ -1139,7 +1139,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform the state-dict of an optimizer corresponding to a sharded model.</p>
 <p>The given state-dict can be transformed to one of three types:
 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.</p>
@@ -1213,7 +1213,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict_to_load">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">optim_state_dict_to_load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_named_optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_directly</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.optim_state_dict_to_load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict_to_load" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">optim_state_dict_to_load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_named_optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_directly</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.optim_state_dict_to_load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict_to_load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.</p>
 <p>Given a <code class="docutils literal notranslate"><span class="pre">optim_state_dict</span></code> that is transformed through
 <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">optim_state_dict()</span></code></a>, it gets converted to the flattened optimizer
@@ -1284,7 +1284,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.register_comm_hook">
-<span class="sig-name descname"><span class="pre">register_comm_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.register_comm_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1987"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.register_comm_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_comm_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.register_comm_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1987"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.register_comm_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a communication hook.</p>
 <p>This is an enhancement that provides a flexible hook to users where they can specify how FSDP aggregates
 gradients across multiple workers.
@@ -1330,7 +1330,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.rekey_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">rekey_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_key_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.rekey_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1676"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.rekey_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">rekey_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_key_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.rekey_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1676"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.rekey_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Re-keys the optimizer state dict <code class="docutils literal notranslate"><span class="pre">optim_state_dict</span></code> to use the key type <code class="docutils literal notranslate"><span class="pre">optim_state_key_type</span></code>.</p>
 <p>This can be used to achieve compatibility between optimizer state dicts from models with FSDP
 instances and ones without.</p>
@@ -1367,7 +1367,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.scatter_full_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scatter_full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.scatter_full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.scatter_full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scatter_full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.scatter_full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.scatter_full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatter the full optimizer state dict from rank 0 to all other ranks.</p>
 <p>Returns the sharded optimizer state dict on each rank.
 The return value is the same as <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">shard_full_optim_state_dict()</span></code></a>, and on rank
@@ -1432,7 +1432,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.set_state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.set_state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the <code class="docutils literal notranslate"><span class="pre">state_dict_type</span></code> of all the descendant FSDP modules of the target module.</p>
 <p>Also takes (optional) configuration for the model’s and optimizer’s state dict.
 The target module does not have to be a FSDP module. If the target
@@ -1486,7 +1486,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shard_full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.shard_full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1474"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shard_full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.shard_full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1474"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Shard a full optimizer state-dict.</p>
 <p>Remaps the state in <code class="docutils literal notranslate"><span class="pre">full_optim_state_dict</span></code> to flattened parameters instead of unflattened
 parameters and restricts to only this rank’s part of the optimizer state.
@@ -1549,7 +1549,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.sharded_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sharded_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.sharded_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1440"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.sharded_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sharded_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.sharded_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1440"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.sharded_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the optimizer state-dict in its sharded form.</p>
 <p>The API is similar to <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">full_optim_state_dict()</span></code></a> but this API chunks
 all non-zero-dimension states to <code class="xref py py-class docutils literal notranslate"><span class="pre">ShardedTensor</span></code> to save memory.
@@ -1570,7 +1570,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.state_dict_type">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.state_dict_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.state_dict_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the <code class="docutils literal notranslate"><span class="pre">state_dict_type</span></code> of all the descendant FSDP modules of the target module.</p>
 <p>This context manager has the same functions as <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type" title="torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_state_dict_type()</span></code></a>. Read the document of
 <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type" title="torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_state_dict_type()</span></code></a> for the detail.</p>
@@ -1602,7 +1602,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summon_full_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">writeback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.summon_full_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/fully_sharded_data_parallel.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summon_full_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">writeback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.summon_full_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params" title="Permalink to this definition">¶</a></dt>
 <dd><p>Expose full params for FSDP instances with this context manager.</p>
 <p>Can be useful <em>after</em> forward/backward for a model to get
 the params for additional processing or checking. It can take a non-FSDP
@@ -1692,7 +1692,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.BackwardPrefetch">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">BackwardPrefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#BackwardPrefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.BackwardPrefetch" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">BackwardPrefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#BackwardPrefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.BackwardPrefetch" title="Permalink to this definition">¶</a></dt>
 <dd><p>This configures explicit backward prefetching, which improves throughput by
 enabling communication and computation overlap in the backward pass at the
 cost of slightly increased memory usage.</p>
@@ -1724,7 +1724,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.ShardingStrategy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardingStrategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardingStrategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardingStrategy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardingStrategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardingStrategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardingStrategy" title="Permalink to this definition">¶</a></dt>
 <dd><p>This specifies the sharding strategy to be used for distributed training by
 <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="torch.distributed.fsdp.FullyShardedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code></a>.</p>
 <ul class="simple">
@@ -1759,7 +1759,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.MixedPrecision">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">MixedPrecision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_low_precision_grads=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_forward_inputs=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_root_forward_inputs=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_classes_to_ignore=(&lt;class</span> <span class="pre">'torch.nn.modules.batchnorm._BatchNorm'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#MixedPrecision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.MixedPrecision" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">MixedPrecision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_low_precision_grads=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_forward_inputs=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_root_forward_inputs=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_classes_to_ignore=(&lt;class</span> <span class="pre">'torch.nn.modules.batchnorm._BatchNorm'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#MixedPrecision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.MixedPrecision" title="Permalink to this definition">¶</a></dt>
 <dd><p>This configures FSDP-native mixed precision training.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Variables</dt>
@@ -1883,7 +1883,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.CPUOffload">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">CPUOffload</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#CPUOffload"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.CPUOffload" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">CPUOffload</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#CPUOffload"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.CPUOffload" title="Permalink to this definition">¶</a></dt>
 <dd><p>This configures CPU offloading.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Variables</dt>
@@ -1897,7 +1897,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.StateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">StateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#StateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.StateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">StateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#StateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.StateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">StateDictConfig</span></code> is the base class for all <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> configuration
 classes. Users should instantiate a child class (e.g.
 <code class="docutils literal notranslate"><span class="pre">FullStateDictConfig</span></code>) in order to configure settings for the
@@ -1913,7 +1913,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#FullStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#FullStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">FullStateDictConfig</span></code> is a config class meant to be used with
 <code class="docutils literal notranslate"><span class="pre">StateDictType.FULL_STATE_DICT</span></code>. We recommend enabling both
 <code class="docutils literal notranslate"><span class="pre">offload_to_cpu=True</span></code> and <code class="docutils literal notranslate"><span class="pre">rank0_only=True</span></code> when saving full state
@@ -1949,7 +1949,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.ShardedStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardedStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardedStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardedStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardedStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardedStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardedStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">ShardedStateDictConfig</span></code> is a config class meant to be used with
 <code class="docutils literal notranslate"><span class="pre">StateDictType.SHARDED_STATE_DICT</span></code>.</p>
 <dl class="field-list simple">
@@ -1969,14 +1969,14 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.LocalStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">LocalStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#LocalStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.LocalStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">LocalStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#LocalStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.LocalStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.OptimStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">OptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#OptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.OptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">OptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#OptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.OptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">OptimStateDictConfig</span></code> is the base class for all <code class="docutils literal notranslate"><span class="pre">optim_state_dict</span></code>
 configuration classes.  Users should instantiate a child class (e.g.
 <code class="docutils literal notranslate"><span class="pre">FullOptimStateDictConfig</span></code>) in order to configure settings for the
@@ -1993,7 +1993,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullOptimStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#FullOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#FullOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Variables</dt>
 <dd class="field-odd"><p><strong>rank0_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, then only rank 0 saves the full state
@@ -2005,7 +2005,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.ShardedOptimStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardedOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardedOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardedOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardedOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardedOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardedOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">ShardedOptimStateDictConfig</span></code> is a config class meant to be used with
 <code class="docutils literal notranslate"><span class="pre">StateDictType.SHARDED_STATE_DICT</span></code>.</p>
 <dl class="field-list simple">
@@ -2025,14 +2025,14 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.LocalOptimStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">LocalOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#LocalOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.LocalOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">LocalOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#LocalOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.LocalOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.StateDictSettings">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">StateDictSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.fsdp.api.StateDictType</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.distributed.fsdp.StateDictConfig" title="torch.distributed.fsdp.api.StateDictConfig"><span class="pre">torch.distributed.fsdp.api.StateDictConfig</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.distributed.fsdp.OptimStateDictConfig" title="torch.distributed.fsdp.api.OptimStateDictConfig"><span class="pre">torch.distributed.fsdp.api.OptimStateDictConfig</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#StateDictSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/fsdp/api.py#L408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.StateDictSettings" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">StateDictSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.fsdp.api.StateDictType</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.distributed.fsdp.StateDictConfig" title="torch.distributed.fsdp.api.StateDictConfig"><span class="pre">torch.distributed.fsdp.api.StateDictConfig</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.distributed.fsdp.OptimStateDictConfig" title="torch.distributed.fsdp.api.OptimStateDictConfig"><span class="pre">torch.distributed.fsdp.api.OptimStateDictConfig</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#StateDictSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.StateDictSettings" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
diff --git a/main/func.api.html b/main/func.api.html
index b487d6464b2..6dbce562321 100644
--- a/main/func.api.html
+++ b/main/func.api.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.batch_norm.html b/main/func.batch_norm.html
index 3d1d68409c7..3238a1c0699 100644
--- a/main/func.batch_norm.html
+++ b/main/func.batch_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.html b/main/func.html
index d4be415ec99..4970177eb90 100644
--- a/main/func.html
+++ b/main/func.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.migrating.html b/main/func.migrating.html
index 3300b698de1..c26240d9346 100644
--- a/main/func.migrating.html
+++ b/main/func.migrating.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.ux_limitations.html b/main/func.ux_limitations.html
index 3305a583d23..e72fe44df29 100644
--- a/main/func.ux_limitations.html
+++ b/main/func.ux_limitations.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.whirlwind_tour.html b/main/func.whirlwind_tour.html
index 1a43dd1492a..8ef4e6c8fc1 100644
--- a/main/func.whirlwind_tour.html
+++ b/main/func.whirlwind_tour.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/future_mod.html b/main/future_mod.html
index f4ffdcd48d7..7260731ae67 100644
--- a/main/future_mod.html
+++ b/main/future_mod.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="torch-future"></span><h1>torch.__future__<a class="headerlink" href="#module-torch.__future__" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__future__.set_overwrite_module_params_on_conversion">
-<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__future__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__future__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether to assign new tensors to the parameters instead of changing the
 existing parameters in-place when converting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>.</p>
 <p>When enabled, the following methods will assign new parameters to the module:</p>
@@ -629,7 +629,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__future__.get_overwrite_module_params_on_conversion">
-<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__future__.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__future__.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether to assign new tensors to the parameters instead of changing the
 existing parameters in-place when converting an <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
 <p>See <a class="reference internal" href="#torch.__future__.set_overwrite_module_params_on_conversion" title="torch.__future__.set_overwrite_module_params_on_conversion"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_overwrite_module_params_on_conversion()</span></code></a> for more information.</p>
@@ -642,7 +642,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__future__.set_swap_module_params_on_conversion">
-<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__future__.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__future__.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether to use <a class="reference internal" href="generated/torch.utils.swap_tensors.html#torch.utils.swap_tensors" title="torch.utils.swap_tensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">swap_tensors()</span></code></a> instead of setting <code class="docutils literal notranslate"><span class="pre">.data</span></code> to
 change the existing parameters in-place when converting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> and instead
 of <code class="docutils literal notranslate"><span class="pre">param.copy_(state_dict[key])</span></code> when loading a state dict into an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>.</p>
@@ -675,7 +675,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__future__.get_swap_module_params_on_conversion">
-<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__future__.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__future__.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether to use <a class="reference internal" href="generated/torch.utils.swap_tensors.html#torch.utils.swap_tensors" title="torch.utils.swap_tensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">swap_tensors()</span></code></a> instead of setting .data to
 change the existing parameters in-place when converting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
 <p>See <a class="reference internal" href="#torch.__future__.set_swap_module_params_on_conversion" title="torch.__future__.set_swap_module_params_on_conversion"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_swap_module_params_on_conversion()</span></code></a> for more information.</p>
diff --git a/main/futures.html b/main/futures.html
index 1d4c89a00e0..c10b21db71a 100644
--- a/main/futures.html
+++ b/main/futures.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -627,7 +627,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.add_done_callback">
-<span class="sig-name descname"><span class="pre">add_done_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.add_done_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/futures/__init__.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.add_done_callback" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_done_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.add_done_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.add_done_callback" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append the given callback function to this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will be run
 when the <code class="docutils literal notranslate"><span class="pre">Future</span></code> is completed.  Multiple callbacks can be added to
 the same <code class="docutils literal notranslate"><span class="pre">Future</span></code>, but the order in which they will be executed cannot
@@ -675,7 +675,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.done">
-<span class="sig-name descname"><span class="pre">done</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.done"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/futures/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.done" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">done</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.done"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.done" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return <code class="docutils literal notranslate"><span class="pre">True</span></code> if this <code class="docutils literal notranslate"><span class="pre">Future</span></code> is done. A <code class="docutils literal notranslate"><span class="pre">Future</span></code> is done if it
 has a result or an exception.</p>
 <p>If the value contains tensors that reside on GPUs, <code class="docutils literal notranslate"><span class="pre">Future.done()</span></code>
@@ -692,7 +692,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.set_exception">
-<span class="sig-name descname"><span class="pre">set_exception</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.set_exception"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/futures/__init__.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.set_exception" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_exception</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.set_exception"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.set_exception" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set an exception for this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will mark this <code class="docutils literal notranslate"><span class="pre">Future</span></code> as
 completed with an error and trigger all attached callbacks. Note that
 when calling wait()/value() on this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, the exception set here
@@ -717,7 +717,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.set_result">
-<span class="sig-name descname"><span class="pre">set_result</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.set_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/futures/__init__.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.set_result" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_result</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.set_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.set_result" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the result for this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will mark this <code class="docutils literal notranslate"><span class="pre">Future</span></code> as
 completed and trigger all attached callbacks. Note that a <code class="docutils literal notranslate"><span class="pre">Future</span></code>
 cannot be marked completed twice.</p>
@@ -759,7 +759,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.then">
-<span class="sig-name descname"><span class="pre">then</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.then"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/futures/__init__.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.then" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">then</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.then"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.then" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append the given callback function to this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will be run
 when the <code class="docutils literal notranslate"><span class="pre">Future</span></code> is completed.  Multiple callbacks can be added to
 the same <code class="docutils literal notranslate"><span class="pre">Future</span></code>, but the order in which they will be executed cannot
@@ -830,7 +830,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.value">
-<span class="sig-name descname"><span class="pre">value</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/futures/__init__.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.value" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">value</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.value" title="Permalink to this definition">¶</a></dt>
 <dd><p>Obtain the value of an already-completed future.</p>
 <p>This method should only be called after a call to <a class="reference internal" href="#torch.futures.Future.wait" title="torch.futures.Future.wait"><code class="xref py py-meth docutils literal notranslate"><span class="pre">wait()</span></code></a> has
 completed, or inside a callback function passed to <a class="reference internal" href="#torch.futures.Future.then" title="torch.futures.Future.then"><code class="xref py py-meth docutils literal notranslate"><span class="pre">then()</span></code></a>. In
@@ -854,7 +854,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/futures/__init__.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Block until the value of this <code class="docutils literal notranslate"><span class="pre">Future</span></code> is ready.</p>
 <p>If the value contains tensors that reside on GPUs, then an additional
 synchronization is performed with the kernels (executing on the device)
@@ -881,7 +881,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.futures.collect_all">
-<span class="sig-prename descclassname"><span class="pre">torch.futures.</span></span><span class="sig-name descname"><span class="pre">collect_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">futures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#collect_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/futures/__init__.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.collect_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.futures.</span></span><span class="sig-name descname"><span class="pre">collect_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">futures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#collect_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.collect_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Collects the provided <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> objects into a single
 combined <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> that is completed when all of the
 sub-futures are completed.</p>
@@ -916,7 +916,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.futures.wait_all">
-<span class="sig-prename descclassname"><span class="pre">torch.futures.</span></span><span class="sig-name descname"><span class="pre">wait_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">futures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#wait_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/futures/__init__.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.wait_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.futures.</span></span><span class="sig-name descname"><span class="pre">wait_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">futures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#wait_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.wait_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits for all provided futures to be complete, and returns
 the list of completed values. If any of the futures encounters an error,
 the method will exit early and report the error not waiting for other
diff --git a/main/fx.experimental.html b/main/fx.experimental.html
index 700c461aa26..54c64936c8e 100644
--- a/main/fx.experimental.html
+++ b/main/fx.experimental.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/fx.html b/main/fx.html
index de52776f414..dbbfcdcfaf4 100644
--- a/main/fx.html
+++ b/main/fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1737,7 +1737,7 @@ <h3>Miscellanea<a class="headerlink" href="#miscellanea" title="Permalink to thi
 <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.symbolic_trace">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">symbolic_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#symbolic_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L1241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.symbolic_trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">symbolic_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#symbolic_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L1241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.symbolic_trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Symbolic tracing API</p>
 <p>Given an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> or function instance <code class="docutils literal notranslate"><span class="pre">root</span></code>, this function will return a <code class="docutils literal notranslate"><span class="pre">GraphModule</span></code>
 constructed by recording operations seen while tracing through <code class="docutils literal notranslate"><span class="pre">root</span></code>.</p>
@@ -1796,7 +1796,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.wrap">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">wrap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn_or_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#wrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L1177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.wrap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">wrap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn_or_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#wrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L1177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.wrap" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function can be called at module-level scope to register fn_or_name as a “leaf function”.
 A “leaf function” will be preserved as a CallFunction node in the FX trace instead of being
 traced through:</p>
@@ -1838,7 +1838,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.GraphModule">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">GraphModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph_module.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">GraphModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule" title="Permalink to this definition">¶</a></dt>
 <dd><p>GraphModule is an nn.Module generated from an fx.Graph. Graphmodule has a
 <code class="docutils literal notranslate"><span class="pre">graph</span></code> attribute, as well as <code class="docutils literal notranslate"><span class="pre">code</span></code> and <code class="docutils literal notranslate"><span class="pre">forward</span></code> attributes generated
 from that <code class="docutils literal notranslate"><span class="pre">graph</span></code>.</p>
@@ -1855,7 +1855,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'GraphModule'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph_module.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'GraphModule'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Construct a GraphModule.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1882,7 +1882,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.add_submodule">
-<span class="sig-name descname"><span class="pre">add_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.add_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph_module.py#L645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.add_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.add_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.add_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds the given submodule to <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>This installs empty Modules where none exist yet if they are
 subpaths of <code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
@@ -1925,7 +1925,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.delete_all_unused_submodules">
-<span class="sig-name descname"><span class="pre">delete_all_unused_submodules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.delete_all_unused_submodules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph_module.py#L727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.delete_all_unused_submodules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">delete_all_unused_submodules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.delete_all_unused_submodules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.delete_all_unused_submodules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Deletes all unused submodules from <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>A Module is considered “used” if any one of the following is
 true:
@@ -1945,7 +1945,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.delete_submodule">
-<span class="sig-name descname"><span class="pre">delete_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.delete_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph_module.py#L685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.delete_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">delete_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.delete_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.delete_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Deletes the given submodule from <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>The module will not be deleted if <code class="docutils literal notranslate"><span class="pre">target</span></code> is not a valid
 target.</p>
@@ -1982,7 +1982,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.print_readable">
-<span class="sig-name descname"><span class="pre">print_readable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">print_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.print_readable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph_module.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.print_readable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_readable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">print_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.print_readable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.print_readable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the Python code generated for current GraphModule and its children GraphModules</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1992,7 +1992,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.recompile">
-<span class="sig-name descname"><span class="pre">recompile</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.recompile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph_module.py#L794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.recompile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">recompile</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.recompile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.recompile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Recompile this GraphModule from its <code class="docutils literal notranslate"><span class="pre">graph</span></code> attribute. This should be
 called after editing the contained <code class="docutils literal notranslate"><span class="pre">graph</span></code>, otherwise the generated
 code of this <code class="docutils literal notranslate"><span class="pre">GraphModule</span></code> will be out of date.</p>
@@ -2009,7 +2009,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.to_folder">
-<span class="sig-name descname"><span class="pre">to_folder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'FxModule'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.to_folder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph_module.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.to_folder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_folder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'FxModule'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.to_folder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.to_folder" title="Permalink to this definition">¶</a></dt>
 <dd><dl>
 <dt>Dumps out module to <code class="docutils literal notranslate"><span class="pre">folder</span></code> with <code class="docutils literal notranslate"><span class="pre">module_name</span></code> so that it can be</dt><dd><p>imported with <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">&lt;folder&gt;</span> <span class="pre">import</span> <span class="pre">&lt;module_name&gt;</span></code></p>
 <p>Args:</p>
@@ -2034,7 +2034,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Graph">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owning_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_cls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_extras</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L924"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owning_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_cls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_extras</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L924"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">Graph</span></code> is the main data structure used in the FX Intermediate Representation.
 It consists of a series of <code class="docutils literal notranslate"><span class="pre">Node</span></code> s, each representing callsites (or other
 syntactic constructs). The list of <code class="docutils literal notranslate"><span class="pre">Node</span></code> s, taken together, constitute a
@@ -2083,7 +2083,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owning_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_cls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_extras</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owning_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_cls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_extras</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Construct an empty Graph.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2095,7 +2095,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.call_function">
-<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">the_function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">the_function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">call_function</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> into the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. A <code class="docutils literal notranslate"><span class="pre">call_function</span></code> node
 represents a call to a Python callable, specified by <code class="docutils literal notranslate"><span class="pre">the_function</span></code>.</p>
 <dl class="field-list simple">
@@ -2132,7 +2132,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.call_method">
-<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">call_method</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> into the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. A <code class="docutils literal notranslate"><span class="pre">call_method</span></code> node
 represents a call to a given method on the 0th element of <code class="docutils literal notranslate"><span class="pre">args</span></code>.</p>
 <dl class="field-list simple">
@@ -2169,7 +2169,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1361"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1361"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">call_module</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> into the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. A <code class="docutils literal notranslate"><span class="pre">call_module</span></code> node
 represents a call to the forward() function of a <code class="docutils literal notranslate"><span class="pre">Module</span></code> in the <code class="docutils literal notranslate"><span class="pre">Module</span></code>
 hierarchy.</p>
@@ -2209,7 +2209,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.create_node">
-<span class="sig-name descname"><span class="pre">create_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.create_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.create_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.create_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.create_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">Node</span></code> and add it to the <code class="docutils literal notranslate"><span class="pre">Graph</span></code> at the current insert-point.
 Note that the current insert-point can be set via <a class="reference internal" href="#torch.fx.Graph.inserting_before" title="torch.fx.Graph.inserting_before"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.inserting_before()</span></code></a>
 and <a class="reference internal" href="#torch.fx.Graph.inserting_after" title="torch.fx.Graph.inserting_after"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.inserting_after()</span></code></a>.</p>
@@ -2243,7 +2243,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.eliminate_dead_code">
-<span class="sig-name descname"><span class="pre">eliminate_dead_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_impure_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.eliminate_dead_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.eliminate_dead_code" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">eliminate_dead_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_impure_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.eliminate_dead_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.eliminate_dead_code" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove all dead code from the graph, based on each node’s number of
 users, and whether the nodes have any side effects. The graph must be
 topologically sorted before calling.</p>
@@ -2294,7 +2294,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.erase_node">
-<span class="sig-name descname"><span class="pre">erase_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to_erase</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.erase_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.erase_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">erase_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to_erase</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.erase_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.erase_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Erases a <code class="docutils literal notranslate"><span class="pre">Node</span></code> from the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. Throws an exception if
 there are still users of that node in the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>.</p>
 <dl class="field-list simple">
@@ -2310,7 +2310,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.find_nodes">
-<span class="sig-name descname"><span class="pre">find_nodes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.find_nodes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1024"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.find_nodes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">find_nodes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.find_nodes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1024"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.find_nodes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Allows for fast query of nodes</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2334,7 +2334,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.get_attr">
-<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualified_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.get_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualified_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.get_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node into the Graph. A <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> represents the
 fetch of an attribute from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy.</p>
 <dl class="field-list simple">
@@ -2368,7 +2368,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.graph_copy">
-<span class="sig-name descname"><span class="pre">graph_copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_map</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_output_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.graph_copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1050"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.graph_copy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">graph_copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_map</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_output_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.graph_copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1050"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.graph_copy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy all nodes from a given graph into <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2395,7 +2395,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.inserting_after">
-<span class="sig-name descname"><span class="pre">inserting_after</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.inserting_after"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.inserting_after" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">inserting_after</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.inserting_after"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.inserting_after" title="Permalink to this definition">¶</a></dt>
 <dd><dl>
 <dt>Set the point at which create_node and companion methods will insert into the graph.</dt><dd><p>When used within a ‘with’ statement, this will temporary set the insert point and
 then restore it when the with statement exits:</p>
@@ -2428,7 +2428,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.inserting_before">
-<span class="sig-name descname"><span class="pre">inserting_before</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.inserting_before"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.inserting_before" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">inserting_before</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.inserting_before"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.inserting_before" title="Permalink to this definition">¶</a></dt>
 <dd><dl>
 <dt>Set the point at which create_node and companion methods will insert into the graph.</dt><dd><p>When used within a ‘with’ statement, this will temporary set the insert point and
 then restore it when the with statement exits:</p>
@@ -2461,7 +2461,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.lint">
-<span class="sig-name descname"><span class="pre">lint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.lint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.lint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">lint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.lint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.lint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs various checks on this Graph to make sure it is well-formed. In
 particular:
 - Checks Nodes have correct ownership (owned by this graph)
@@ -2476,7 +2476,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.node_copy">
-<span class="sig-name descname"><span class="pre">node_copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_transform=&lt;function</span> <span class="pre">Graph.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.node_copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.node_copy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">node_copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_transform=&lt;function</span> <span class="pre">Graph.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.node_copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.node_copy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy a node from one graph into another. <code class="docutils literal notranslate"><span class="pre">arg_transform</span></code> needs to transform arguments from
 the graph of node to the graph of self. Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Copying all the nodes in `g` into `new_graph`</span>
@@ -2524,7 +2524,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.on_generate_code">
-<span class="sig-name descname"><span class="pre">on_generate_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">make_transformer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.on_generate_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1873"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.on_generate_code" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">on_generate_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">make_transformer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.on_generate_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1873"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.on_generate_code" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a transformer function when python code is generated</p>
 <blockquote>
 <div><dl>
@@ -2597,7 +2597,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.output">
-<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.output" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.output" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert an <code class="docutils literal notranslate"><span class="pre">output</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> into the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. An <code class="docutils literal notranslate"><span class="pre">output</span></code> node represents
 a <code class="docutils literal notranslate"><span class="pre">return</span></code> statement in Python code. <code class="docutils literal notranslate"><span class="pre">result</span></code> is the value that should
 be returned.</p>
@@ -2623,7 +2623,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.output_node">
-<span class="sig-name descname"><span class="pre">output_node</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.output_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.output_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">output_node</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.output_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.output_node" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
@@ -2637,7 +2637,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.placeholder">
-<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.placeholder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.placeholder" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> node into the Graph. A <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> represents
 a function input.</p>
 <dl class="field-list simple">
@@ -2672,7 +2672,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.print_tabular">
-<span class="sig-name descname"><span class="pre">print_tabular</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.print_tabular"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1673"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.print_tabular" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_tabular</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.print_tabular"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1673"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.print_tabular" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prints the intermediate representation of the graph in tabular
 format. Note that this API requires the <code class="docutils literal notranslate"><span class="pre">tabulate</span></code> module to be
 installed.</p>
@@ -2684,7 +2684,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.process_inputs">
-<span class="sig-name descname"><span class="pre">process_inputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.process_inputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.process_inputs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">process_inputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.process_inputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.process_inputs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Processes args so that they can be passed to the FX graph.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -2694,7 +2694,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.process_outputs">
-<span class="sig-name descname"><span class="pre">process_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.process_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.process_outputs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">process_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.process_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.process_outputs" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
@@ -2703,7 +2703,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.python_code">
-<span class="sig-name descname"><span class="pre">python_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.python_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.python_code" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">python_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.python_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.python_code" title="Permalink to this definition">¶</a></dt>
 <dd><p>Turn this <code class="docutils literal notranslate"><span class="pre">Graph</span></code> into valid Python code.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2726,7 +2726,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.set_codegen">
-<span class="sig-name descname"><span class="pre">set_codegen</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">codegen</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.set_codegen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/graph.py#L1869"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.set_codegen" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_codegen</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">codegen</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.set_codegen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1869"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.set_codegen" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
@@ -2739,7 +2739,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Node">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">Node</span></code> is the data structure that represents individual operations within
 a <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. For the most part, Nodes represent callsites to various entities,
 such as operators, methods, and Modules (some exceptions include nodes that
@@ -2787,7 +2787,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.append">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.append" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.append" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert <code class="docutils literal notranslate"><span class="pre">x</span></code> after this node in the list of nodes in the graph.
 Equivalent to <code class="docutils literal notranslate"><span class="pre">self.next.prepend(x)</span></code></p>
 <dl class="field-list simple">
@@ -2813,7 +2813,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.format_node">
-<span class="sig-name descname"><span class="pre">format_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholder_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maybe_return_typename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.format_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.format_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">format_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholder_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maybe_return_typename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.format_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.format_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a descriptive string representation of <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>This method can be used with no arguments as a debugging
 utility.</p>
@@ -2856,7 +2856,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.insert_arg">
-<span class="sig-name descname"><span class="pre">insert_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.insert_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.insert_arg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">insert_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.insert_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.insert_arg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert an positional argument to the argument list with given index.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2874,7 +2874,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.is_impure">
-<span class="sig-name descname"><span class="pre">is_impure</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.is_impure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L750"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.is_impure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_impure</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.is_impure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L750"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.is_impure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether this op is impure, i.e. if its op is a placeholder or
 output, or if a call_function or call_module which is impure.</p>
 <dl class="field-list simple">
@@ -2914,7 +2914,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.normalized_arguments">
-<span class="sig-name descname"><span class="pre">normalized_arguments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwarg_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize_to_only_use_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.normalized_arguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L788"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.normalized_arguments" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">normalized_arguments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwarg_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize_to_only_use_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.normalized_arguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L788"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.normalized_arguments" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns normalized arguments to Python targets. This means that
 <cite>args/kwargs</cite> will be matched up to the module/functional’s
 signature and return exclusively kwargs in positional order
@@ -2947,7 +2947,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.prepend">
-<span class="sig-name descname"><span class="pre">prepend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.prepend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.prepend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prepend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.prepend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.prepend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert x before this node in the list of nodes in the graph. Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Before</span><span class="p">:</span> <span class="n">p</span> <span class="o">-&gt;</span> <span class="bp">self</span>
         <span class="n">bx</span> <span class="o">-&gt;</span> <span class="n">x</span> <span class="o">-&gt;</span> <span class="n">ax</span>
@@ -2979,7 +2979,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.replace_all_uses_with">
-<span class="sig-name descname"><span class="pre">replace_all_uses_with</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">replace_with</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delete_user_cb=&lt;function</span> <span class="pre">Node.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propagate_meta=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.replace_all_uses_with"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.replace_all_uses_with" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replace_all_uses_with</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">replace_with</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delete_user_cb=&lt;function</span> <span class="pre">Node.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propagate_meta=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.replace_all_uses_with"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.replace_all_uses_with" title="Permalink to this definition">¶</a></dt>
 <dd><p>Replace all uses of <code class="docutils literal notranslate"><span class="pre">self</span></code> in the Graph with the Node <code class="docutils literal notranslate"><span class="pre">replace_with</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3008,7 +3008,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.replace_input_with">
-<span class="sig-name descname"><span class="pre">replace_input_with</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">old_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.replace_input_with"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.replace_input_with" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replace_input_with</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">old_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.replace_input_with"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.replace_input_with" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loop through input nodes of <code class="docutils literal notranslate"><span class="pre">self</span></code>, and replace all instances of
 <code class="docutils literal notranslate"><span class="pre">old_input</span></code> with <code class="docutils literal notranslate"><span class="pre">new_input</span></code>.</p>
 <dl class="field-list simple">
@@ -3039,7 +3039,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.update_arg">
-<span class="sig-name descname"><span class="pre">update_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.update_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.update_arg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.update_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.update_arg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update an existing positional argument to contain the new value
 <code class="docutils literal notranslate"><span class="pre">arg</span></code>. After calling, <code class="docutils literal notranslate"><span class="pre">self.args[idx]</span> <span class="pre">==</span> <span class="pre">arg</span></code>.</p>
 <dl class="field-list simple">
@@ -3058,7 +3058,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.update_kwarg">
-<span class="sig-name descname"><span class="pre">update_kwarg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.update_kwarg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/node.py#L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.update_kwarg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update_kwarg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.update_kwarg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.update_kwarg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update an existing keyword argument to contain the new value
 <code class="docutils literal notranslate"><span class="pre">arg</span></code>. After calling, <code class="docutils literal notranslate"><span class="pre">self.kwargs[key]</span> <span class="pre">==</span> <span class="pre">arg</span></code>.</p>
 <dl class="field-list simple">
@@ -3079,7 +3079,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Tracer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Tracer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">autowrap_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(math,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autowrap_functions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Tracer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">autowrap_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(math,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autowrap_functions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer" title="Permalink to this definition">¶</a></dt>
 <dd><blockquote>
 <div><p><code class="docutils literal notranslate"><span class="pre">Tracer</span></code> is the class that implements the symbolic tracing functionality
 of <code class="docutils literal notranslate"><span class="pre">torch.fx.symbolic_trace</span></code>. A call to <code class="docutils literal notranslate"><span class="pre">symbolic_trace(m)</span></code> is equivalent
@@ -3096,7 +3096,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method that specifies the behavior of this <code class="docutils literal notranslate"><span class="pre">Tracer</span></code> when it encounters
 a call to an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> instance.</p>
 <p>By default, the behavior is to check if the called module is a leaf module
@@ -3132,7 +3132,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.create_arg">
-<span class="sig-name descname"><span class="pre">create_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.create_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L338"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_arg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.create_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L338"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_arg" title="Permalink to this definition">¶</a></dt>
 <dd><p>A method to specify the behavior of tracing when preparing values to
 be used as arguments to nodes in the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>.</p>
 <p>By default, the behavior includes:</p>
@@ -3170,7 +3170,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.create_args_for_root">
-<span class="sig-name descname"><span class="pre">create_args_for_root</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.create_args_for_root"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_args_for_root" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_args_for_root</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.create_args_for_root"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_args_for_root" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> nodes corresponding to the signature of the <code class="docutils literal notranslate"><span class="pre">root</span></code>
 Module. This method introspects root’s signature and emits those
 nodes accordingly, also supporting <code class="docutils literal notranslate"><span class="pre">*args</span></code> and <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code>.</p>
@@ -3182,7 +3182,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.create_node">
-<span class="sig-name descname"><span class="pre">create_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/proxy.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Inserts a graph node given target, args, kwargs, and name.</p>
 <p>This method can be overridden to do extra checking, validation, or
 modification of values used in node creation. For example, one might
@@ -3200,7 +3200,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.create_proxy">
-<span class="sig-name descname"><span class="pre">create_proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proxy_factory_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/proxy.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_proxy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proxy_factory_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_proxy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a Node from the given arguments, then return the Node
 wrapped in a Proxy object.</p>
 <p>If kind = ‘placeholder’, then we’re creating a Node that
@@ -3217,7 +3217,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.get_fresh_qualname">
-<span class="sig-name descname"><span class="pre">get_fresh_qualname</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.get_fresh_qualname"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.get_fresh_qualname" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_fresh_qualname</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.get_fresh_qualname"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.get_fresh_qualname" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gets a fresh name for a prefix and returns it. This function ensures
 that it will not clash with an existing attribute on the graph.</p>
 <div class="admonition note">
@@ -3233,7 +3233,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.getattr">
-<span class="sig-name descname"><span class="pre">getattr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attr_val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_proxy_cache</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.getattr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L538"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.getattr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">getattr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attr_val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_proxy_cache</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.getattr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L538"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.getattr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method that specifies the behavior of this <code class="docutils literal notranslate"><span class="pre">Tracer</span></code> when we call getattr
 on a call to an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> instance.</p>
 <p>By default, the behavior is to return a proxy value for the attribute. It
@@ -3261,7 +3261,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.is_leaf_module">
-<span class="sig-name descname"><span class="pre">is_leaf_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_qualified_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.is_leaf_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.is_leaf_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_leaf_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_qualified_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.is_leaf_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.is_leaf_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>A method to specify whether a given <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> is a “leaf” module.</p>
 <p>Leaf modules are the atomic units that appear in
 the IR, referenced by <code class="docutils literal notranslate"><span class="pre">call_module</span></code> calls. By default,
@@ -3291,7 +3291,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.iter">
-<span class="sig-name descname"><span class="pre">iter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/proxy.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.iter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">iter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.iter" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Called when a proxy object is being iterated over, such as</dt><dd><p>when used in control flow.  Normally we don’t know what to do because
 we don’t know the value of the proxy, but a custom tracer can attach more
@@ -3311,7 +3311,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.keys">
-<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/proxy.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.keys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.keys" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Called when a proxy object is has the keys() method called.</dt><dd><p>This is what happens when ** is called on a proxy. This should return an
 iterator it ** is suppose to work in your custom tracer.</p>
@@ -3330,7 +3330,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.path_of_module">
-<span class="sig-name descname"><span class="pre">path_of_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.path_of_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.path_of_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">path_of_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.path_of_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.path_of_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper method to find the qualified name of <code class="docutils literal notranslate"><span class="pre">mod</span></code> in the Module hierarchy
 of <code class="docutils literal notranslate"><span class="pre">root</span></code>. For example, if <code class="docutils literal notranslate"><span class="pre">root</span></code> has a submodule named <code class="docutils literal notranslate"><span class="pre">foo</span></code>, which has
 a submodule named <code class="docutils literal notranslate"><span class="pre">bar</span></code>, passing <code class="docutils literal notranslate"><span class="pre">bar</span></code> into this function will return
@@ -3351,7 +3351,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.proxy">
-<span class="sig-name descname"><span class="pre">proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/proxy.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.proxy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.proxy" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Backwards-compatibility for this API is guaranteed.</p>
@@ -3365,7 +3365,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.to_bool">
-<span class="sig-name descname"><span class="pre">to_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/proxy.py#L356"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.to_bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.to_bool" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Called when a proxy object is being converted to a boolean, such as</dt><dd><p>when used in control flow.  Normally we don’t know what to do because
 we don’t know the value of the proxy, but a custom tracer can attach more
@@ -3385,7 +3385,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.trace">
-<span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/_symbolic_trace.py#L700"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L700"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Trace <code class="docutils literal notranslate"><span class="pre">root</span></code> and return the corresponding FX <code class="docutils literal notranslate"><span class="pre">Graph</span></code> representation. <code class="docutils literal notranslate"><span class="pre">root</span></code>
 can either be an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> instance or a Python callable.</p>
 <p>Note that after this call, <code class="docutils literal notranslate"><span class="pre">self.root</span></code> may be different from the <code class="docutils literal notranslate"><span class="pre">root</span></code> passed
@@ -3420,7 +3420,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Proxy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/proxy.html#Proxy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/proxy.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Proxy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/proxy.html#Proxy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Proxy" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">Proxy</span></code> objects are <code class="docutils literal notranslate"><span class="pre">Node</span></code> wrappers that flow through the
 program during symbolic tracing and record all the operations
 (<code class="docutils literal notranslate"><span class="pre">torch</span></code> function calls, method calls, operators) that they touch
@@ -3453,7 +3453,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Interpreter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Interpreter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">garbage_collect_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Interpreter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">garbage_collect_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter" title="Permalink to this definition">¶</a></dt>
 <dd><p>An Interpreter executes an FX graph Node-by-Node. This pattern
 can be useful for many things, including writing code
 transformations as well as analysis passes.</p>
@@ -3517,7 +3517,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.boxed_run">
-<span class="sig-name descname"><span class="pre">boxed_run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args_list</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.boxed_run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.boxed_run" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">boxed_run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args_list</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.boxed_run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.boxed_run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run <cite>module</cite> via interpretation and return the result.  This uses the “boxed”
 calling convention, where you pass a list of arguments, which will be cleared
 by the interpreter.  This ensures that input tensors are promptly deallocated.</p>
@@ -3529,7 +3529,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.call_function">
-<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_function</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3557,7 +3557,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.call_method">
-<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_method</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3585,7 +3585,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_module</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3613,7 +3613,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.fetch_args_kwargs_from_env">
-<span class="sig-name descname"><span class="pre">fetch_args_kwargs_from_env</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.fetch_args_kwargs_from_env"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.fetch_args_kwargs_from_env" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fetch_args_kwargs_from_env</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.fetch_args_kwargs_from_env"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.fetch_args_kwargs_from_env" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fetch the concrete values of <code class="docutils literal notranslate"><span class="pre">args</span></code> and <code class="docutils literal notranslate"><span class="pre">kwargs</span></code> of node <code class="docutils literal notranslate"><span class="pre">n</span></code>
 from the current execution environment.</p>
 <dl class="field-list simple">
@@ -3635,7 +3635,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.fetch_attr">
-<span class="sig-name descname"><span class="pre">fetch_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.fetch_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.fetch_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fetch_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.fetch_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.fetch_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fetch an attribute from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy of <code class="docutils literal notranslate"><span class="pre">self.module</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3656,7 +3656,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.get_attr">
-<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.get_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.get_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node. Will retrieve an attribute
 value from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy of <code class="docutils literal notranslate"><span class="pre">self.module</span></code>.</p>
 <dl class="field-list simple">
@@ -3684,7 +3684,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.map_nodes_to_values">
-<span class="sig-name descname"><span class="pre">map_nodes_to_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.map_nodes_to_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.map_nodes_to_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">map_nodes_to_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.map_nodes_to_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.map_nodes_to_values" title="Permalink to this definition">¶</a></dt>
 <dd><p>Recursively descend through <code class="docutils literal notranslate"><span class="pre">args</span></code> and look up the concrete value
 for each <code class="docutils literal notranslate"><span class="pre">Node</span></code> in the current execution environment.</p>
 <dl class="field-list simple">
@@ -3706,7 +3706,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.output">
-<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.output" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.output" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute an <code class="docutils literal notranslate"><span class="pre">output</span></code> node. This really just retrieves
 the value referenced by the <code class="docutils literal notranslate"><span class="pre">output</span></code> node and returns it.</p>
 <dl class="field-list simple">
@@ -3734,7 +3734,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.placeholder">
-<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.placeholder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.placeholder" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> node. Note that this is stateful:
 <code class="docutils literal notranslate"><span class="pre">Interpreter</span></code> maintains an internal iterator over
 arguments passed to <code class="docutils literal notranslate"><span class="pre">run</span></code> and this method returns
@@ -3764,7 +3764,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.run">
-<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_env</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_io_processing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.run" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_env</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_io_processing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run <cite>module</cite> via interpretation and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3793,7 +3793,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.run_node">
-<span class="sig-name descname"><span class="pre">run_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.run_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.run_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.run_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.run_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run a specific node <code class="docutils literal notranslate"><span class="pre">n</span></code> and return the result.
 Calls into placeholder, get_attr, call_function,
 call_method, call_module, or output depending
@@ -3819,7 +3819,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Transformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">Transformer</span></code> is a special type of interpreter that produces a
 new <code class="docutils literal notranslate"><span class="pre">Module</span></code>. It exposes a <code class="docutils literal notranslate"><span class="pre">transform()</span></code> method that returns
 the transformed <code class="docutils literal notranslate"><span class="pre">Module</span></code>. <code class="docutils literal notranslate"><span class="pre">Transformer</span></code> does not require
@@ -3868,7 +3868,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.call_function">
-<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.call_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.call_function" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Backwards-compatibility for this API is guaranteed.</p>
@@ -3882,7 +3882,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Backwards-compatibility for this API is guaranteed.</p>
@@ -3896,7 +3896,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.get_attr">
-<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L534"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.get_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L534"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.get_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node. In <code class="docutils literal notranslate"><span class="pre">Transformer</span></code>, this is
 overridden to insert a new <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node into the output
 graph.</p>
@@ -3922,7 +3922,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.placeholder">
-<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.placeholder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.placeholder" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> node. In <code class="docutils literal notranslate"><span class="pre">Transformer</span></code>, this is
 overridden to insert a new <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> into the output
 graph.</p>
@@ -3948,7 +3948,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.transform" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.transform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform <code class="docutils literal notranslate"><span class="pre">self.module</span></code> and return the transformed
 <code class="docutils literal notranslate"><span class="pre">GraphModule</span></code>.</p>
 <div class="admonition note">
@@ -3966,7 +3966,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.replace_pattern">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">replace_pattern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pattern</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/subgraph_rewriter.html#replace_pattern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/subgraph_rewriter.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.replace_pattern" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">replace_pattern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pattern</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/subgraph_rewriter.html#replace_pattern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/subgraph_rewriter.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.replace_pattern" title="Permalink to this definition">¶</a></dt>
 <dd><p>Matches all possible non-overlapping sets of operators and their
 data dependencies (<code class="docutils literal notranslate"><span class="pre">pattern</span></code>) in the Graph of a GraphModule
 (<code class="docutils literal notranslate"><span class="pre">gm</span></code>), then replaces each of these matched subgraphs with another
diff --git a/main/generated/exportdb/index.html b/main/generated/exportdb/index.html
index 112dd14a762..418fc4c699e 100644
--- a/main/generated/exportdb/index.html
+++ b/main/generated/exportdb/index.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -622,8 +622,8 @@
 <p class="caption" role="heading"><span class="caption-text">Tags</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="torch.escape-hatch.html">torch.escape-hatch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="torch.dynamic-shape.html">torch.dynamic-shape</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch.cond.html">torch.cond</a></li>
+<li class="toctree-l1"><a class="reference internal" href="torch.dynamic-shape.html">torch.dynamic-shape</a></li>
 <li class="toctree-l1"><a class="reference internal" href="python.closure.html">python.closure</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch.dynamic-value.html">torch.dynamic-value</a></li>
 <li class="toctree-l1"><a class="reference internal" href="python.data-structure.html">python.data-structure</a></li>
@@ -792,7 +792,7 @@ <h3>class_method<a class="headerlink" href="#class-method" title="Permalink to t
 <h3>cond_branch_class_method<a class="headerlink" href="#cond-branch-class-method" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -861,7 +861,7 @@ <h3>cond_branch_class_method<a class="headerlink" href="#cond-branch-class-metho
 <h3>cond_branch_nested_function<a class="headerlink" href="#cond-branch-nested-function" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -927,7 +927,7 @@ <h3>cond_branch_nested_function<a class="headerlink" href="#cond-branch-nested-f
 <h3>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlocal-variables" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1017,7 +1017,7 @@ <h3>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlo
 <h3>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-variable" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1079,7 +1079,7 @@ <h3>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-varia
 <h3>cond_operands<a class="headerlink" href="#cond-operands" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1157,7 +1157,7 @@ <h3>cond_operands<a class="headerlink" href="#cond-operands" title="Permalink to
 <h3>cond_predicate<a class="headerlink" href="#cond-predicate" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1207,7 +1207,7 @@ <h3>cond_predicate<a class="headerlink" href="#cond-predicate" title="Permalink
 <h3>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-example" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1267,7 +1267,7 @@ <h3>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-exam
 <h3>constrain_as_value_example<a class="headerlink" href="#constrain-as-value-example" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1546,7 +1546,7 @@ <h3>dynamic_shape_if_guard<a class="headerlink" href="#dynamic-shape-if-guard" t
 <h3>dynamic_shape_map<a class="headerlink" href="#dynamic-shape-map" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.map.html"><span class="doc">torch.map</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.map.html"><span class="doc">torch.map</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1746,7 +1746,7 @@ <h3>fn_with_kwargs<a class="headerlink" href="#fn-with-kwargs" title="Permalink
 <h3>list_contains<a class="headerlink" href="#list-contains" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a>, <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
+<p>Tags: <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -2460,7 +2460,7 @@ <h3>unsupported_operator<a class="headerlink" href="#unsupported-operator" title
 </pre></div>
 </div>
 <p>Result:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Unsupported</span><span class="p">:</span> <span class="n">torch</span><span class="o">.*</span> <span class="n">op</span> <span class="n">returned</span> <span class="n">non</span><span class="o">-</span><span class="n">Tensor</span> <span class="nb">int</span> <span class="n">call_function</span> <span class="o">&lt;</span><span class="n">function</span> <span class="n">sym_min</span> <span class="n">at</span> <span class="mh">0x7f6a40c213a0</span><span class="o">&gt;</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Unsupported</span><span class="p">:</span> <span class="n">torch</span><span class="o">.*</span> <span class="n">op</span> <span class="n">returned</span> <span class="n">non</span><span class="o">-</span><span class="n">Tensor</span> <span class="nb">int</span> <span class="n">call_function</span> <span class="o">&lt;</span><span class="n">function</span> <span class="n">sym_min</span> <span class="n">at</span> <span class="mh">0x7ff65abf64c0</span><span class="o">&gt;</span>
 </pre></div>
 </div>
 </div>
diff --git a/main/generated/exportdb/python.assert.html b/main/generated/exportdb/python.assert.html
index 1761769bbab..1840fd25642 100644
--- a/main/generated/exportdb/python.assert.html
+++ b/main/generated/exportdb/python.assert.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -658,7 +658,7 @@ <h2>dynamic_shape_assert<a class="headerlink" href="#dynamic-shape-assert" title
 <h2>list_contains<a class="headerlink" href="#list-contains" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a>, <a class="reference internal" href="#"><span class="doc">python.assert</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">python.assert</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a></p>
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 <p>Original source code:</p>
diff --git a/main/generated/exportdb/python.builtin.html b/main/generated/exportdb/python.builtin.html
index 47e482b9c39..f22a4db0c21 100644
--- a/main/generated/exportdb/python.builtin.html
+++ b/main/generated/exportdb/python.builtin.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/generated/exportdb/python.closure.html b/main/generated/exportdb/python.closure.html
index d806c1d15b9..bbffffd91d6 100644
--- a/main/generated/exportdb/python.closure.html
+++ b/main/generated/exportdb/python.closure.html
@@ -42,7 +42,7 @@
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="torch.dynamic-value" href="torch.dynamic-value.html" />
-    <link rel="prev" title="torch.cond" href="torch.cond.html" />
+    <link rel="prev" title="torch.dynamic-shape" href="torch.dynamic-shape.html" />
 
 <!--
   Search engines should not index the main version of documentation.
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@ <h1>python.closure<a class="headerlink" href="#python-closure" title="Permalink
 <h2>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-variable" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">python.closure</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="#"><span class="doc">python.closure</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -742,7 +742,7 @@ <h2>nested_function<a class="headerlink" href="#nested-function" title="Permalin
         <a href="torch.dynamic-value.html" class="btn btn-neutral float-right" title="torch.dynamic-value" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
-        <a href="torch.cond.html" class="btn btn-neutral" title="torch.cond" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
+        <a href="torch.dynamic-shape.html" class="btn btn-neutral" title="torch.dynamic-shape" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
       
     </div>
   
diff --git a/main/generated/exportdb/python.context-manager.html b/main/generated/exportdb/python.context-manager.html
index 633f954d5d7..71cec6fd011 100644
--- a/main/generated/exportdb/python.context-manager.html
+++ b/main/generated/exportdb/python.context-manager.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/python.control-flow.html b/main/generated/exportdb/python.control-flow.html
index b4073b88f95..d6ab5bcfa1d 100644
--- a/main/generated/exportdb/python.control-flow.html
+++ b/main/generated/exportdb/python.control-flow.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/python.data-structure.html b/main/generated/exportdb/python.data-structure.html
index 5546af678af..b472b9e918d 100644
--- a/main/generated/exportdb/python.data-structure.html
+++ b/main/generated/exportdb/python.data-structure.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -724,7 +724,7 @@ <h2>fn_with_kwargs<a class="headerlink" href="#fn-with-kwargs" title="Permalink
 <h2>list_contains<a class="headerlink" href="#list-contains" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">python.data-structure</span></a>, <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
+<p>Tags: <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">python.data-structure</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/python.object-model.html b/main/generated/exportdb/python.object-model.html
index e965de42793..6b1032cf81e 100644
--- a/main/generated/exportdb/python.object-model.html
+++ b/main/generated/exportdb/python.object-model.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/torch.cond.html b/main/generated/exportdb/torch.cond.html
index 90b48a74244..7bd14810780 100644
--- a/main/generated/exportdb/torch.cond.html
+++ b/main/generated/exportdb/torch.cond.html
@@ -41,8 +41,8 @@
   <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="python.closure" href="python.closure.html" />
-    <link rel="prev" title="torch.dynamic-shape" href="torch.dynamic-shape.html" />
+    <link rel="next" title="torch.dynamic-shape" href="torch.dynamic-shape.html" />
+    <link rel="prev" title="torch.escape-hatch" href="torch.escape-hatch.html" />
 
 <!--
   Search engines should not index the main version of documentation.
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@ <h1>torch.cond<a class="headerlink" href="#torch-cond" title="Permalink to this
 <h2>cond_branch_class_method<a class="headerlink" href="#cond-branch-class-method" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -685,7 +685,7 @@ <h2>cond_branch_class_method<a class="headerlink" href="#cond-branch-class-metho
 <h2>cond_branch_nested_function<a class="headerlink" href="#cond-branch-nested-function" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -751,7 +751,7 @@ <h2>cond_branch_nested_function<a class="headerlink" href="#cond-branch-nested-f
 <h2>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlocal-variables" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -841,7 +841,7 @@ <h2>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlo
 <h2>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-variable" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -903,7 +903,7 @@ <h2>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-varia
 <h2>cond_operands<a class="headerlink" href="#cond-operands" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -981,7 +981,7 @@ <h2>cond_operands<a class="headerlink" href="#cond-operands" title="Permalink to
 <h2>cond_predicate<a class="headerlink" href="#cond-predicate" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1037,10 +1037,10 @@ <h2>cond_predicate<a class="headerlink" href="#cond-predicate" title="Permalink
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="python.closure.html" class="btn btn-neutral float-right" title="python.closure" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
+        <a href="torch.dynamic-shape.html" class="btn btn-neutral float-right" title="torch.dynamic-shape" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
-        <a href="torch.dynamic-shape.html" class="btn btn-neutral" title="torch.dynamic-shape" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
+        <a href="torch.escape-hatch.html" class="btn btn-neutral" title="torch.escape-hatch" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
       
     </div>
   
diff --git a/main/generated/exportdb/torch.dynamic-shape.html b/main/generated/exportdb/torch.dynamic-shape.html
index 2d97f8fe3a7..8c9fc1e35c0 100644
--- a/main/generated/exportdb/torch.dynamic-shape.html
+++ b/main/generated/exportdb/torch.dynamic-shape.html
@@ -41,8 +41,8 @@
   <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="torch.cond" href="torch.cond.html" />
-    <link rel="prev" title="torch.escape-hatch" href="torch.escape-hatch.html" />
+    <link rel="next" title="python.closure" href="python.closure.html" />
+    <link rel="prev" title="torch.cond" href="torch.cond.html" />
 
 <!--
   Search engines should not index the main version of documentation.
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@ <h1>torch.dynamic-shape<a class="headerlink" href="#torch-dynamic-shape" title="
 <h2>cond_branch_class_method<a class="headerlink" href="#cond-branch-class-method" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -685,7 +685,7 @@ <h2>cond_branch_class_method<a class="headerlink" href="#cond-branch-class-metho
 <h2>cond_branch_nested_function<a class="headerlink" href="#cond-branch-nested-function" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -751,7 +751,7 @@ <h2>cond_branch_nested_function<a class="headerlink" href="#cond-branch-nested-f
 <h2>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlocal-variables" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -841,7 +841,7 @@ <h2>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlo
 <h2>cond_operands<a class="headerlink" href="#cond-operands" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -919,7 +919,7 @@ <h2>cond_operands<a class="headerlink" href="#cond-operands" title="Permalink to
 <h2>cond_predicate<a class="headerlink" href="#cond-predicate" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1053,7 +1053,7 @@ <h2>dynamic_shape_if_guard<a class="headerlink" href="#dynamic-shape-if-guard" t
 <h2>dynamic_shape_map<a class="headerlink" href="#dynamic-shape-map" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="torch.map.html"><span class="doc">torch.map</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.map.html"><span class="doc">torch.map</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1228,7 +1228,7 @@ <h2>dynamic_shape_view<a class="headerlink" href="#dynamic-shape-view" title="Pe
 <h2>list_contains<a class="headerlink" href="#list-contains" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a>, <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a></p>
+<p>Tags: <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1327,10 +1327,10 @@ <h2>scalar_output<a class="headerlink" href="#scalar-output" title="Permalink to
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="torch.cond.html" class="btn btn-neutral float-right" title="torch.cond" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
+        <a href="python.closure.html" class="btn btn-neutral float-right" title="python.closure" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
-        <a href="torch.escape-hatch.html" class="btn btn-neutral" title="torch.escape-hatch" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
+        <a href="torch.cond.html" class="btn btn-neutral" title="torch.cond" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
       
     </div>
   
diff --git a/main/generated/exportdb/torch.dynamic-value.html b/main/generated/exportdb/torch.dynamic-value.html
index 361f63f2982..70972b26611 100644
--- a/main/generated/exportdb/torch.dynamic-value.html
+++ b/main/generated/exportdb/torch.dynamic-value.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@ <h1>torch.dynamic-value<a class="headerlink" href="#torch-dynamic-value" title="
 <h2>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-example" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-value</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -676,7 +676,7 @@ <h2>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-exam
 <h2>constrain_as_value_example<a class="headerlink" href="#constrain-as-value-example" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-value</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/torch.escape-hatch.html b/main/generated/exportdb/torch.escape-hatch.html
index 83ce3a89b9c..10820754d5c 100644
--- a/main/generated/exportdb/torch.escape-hatch.html
+++ b/main/generated/exportdb/torch.escape-hatch.html
@@ -41,7 +41,7 @@
   <link rel="stylesheet" href="../../_static/css/custom.css" type="text/css" />
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="torch.dynamic-shape" href="torch.dynamic-shape.html" />
+    <link rel="next" title="torch.cond" href="torch.cond.html" />
     <link rel="prev" title="ExportDB" href="index.html" />
 
 <!--
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -661,7 +661,7 @@ <h2>assume_constant_result<a class="headerlink" href="#assume-constant-result" t
 <h2>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-example" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="#"><span class="doc">torch.escape-hatch</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -721,7 +721,7 @@ <h2>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-exam
 <h2>constrain_as_value_example<a class="headerlink" href="#constrain-as-value-example" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="#"><span class="doc">torch.escape-hatch</span></a></p>
 <p>Support Level: SUPPORTED</p>
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 <p>Original source code:</p>
@@ -786,7 +786,7 @@ <h2>constrain_as_value_example<a class="headerlink" href="#constrain-as-value-ex
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="torch.dynamic-shape.html" class="btn btn-neutral float-right" title="torch.dynamic-shape" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
+        <a href="torch.cond.html" class="btn btn-neutral float-right" title="torch.cond" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
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diff --git a/main/generated/exportdb/torch.map.html b/main/generated/exportdb/torch.map.html
index 21f55891030..5d382275225 100644
--- a/main/generated/exportdb/torch.map.html
+++ b/main/generated/exportdb/torch.map.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@ <h1>torch.map<a class="headerlink" href="#torch-map" title="Permalink to this he
 <h2>dynamic_shape_map<a class="headerlink" href="#dynamic-shape-map" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.map</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.map</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/torch.mutation.html b/main/generated/exportdb/torch.mutation.html
index 783f0928456..436f81b4a1c 100644
--- a/main/generated/exportdb/torch.mutation.html
+++ b/main/generated/exportdb/torch.mutation.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/torch.operator.html b/main/generated/exportdb/torch.operator.html
index b8383f83976..6e5f159d34b 100644
--- a/main/generated/exportdb/torch.operator.html
+++ b/main/generated/exportdb/torch.operator.html
@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -644,7 +644,7 @@ <h2>unsupported_operator<a class="headerlink" href="#unsupported-operator" title
 </pre></div>
 </div>
 <p>Result:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Unsupported</span><span class="p">:</span> <span class="n">torch</span><span class="o">.*</span> <span class="n">op</span> <span class="n">returned</span> <span class="n">non</span><span class="o">-</span><span class="n">Tensor</span> <span class="nb">int</span> <span class="n">call_function</span> <span class="o">&lt;</span><span class="n">function</span> <span class="n">sym_min</span> <span class="n">at</span> <span class="mh">0x7f6a40c213a0</span><span class="o">&gt;</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Unsupported</span><span class="p">:</span> <span class="n">torch</span><span class="o">.*</span> <span class="n">op</span> <span class="n">returned</span> <span class="n">non</span><span class="o">-</span><span class="n">Tensor</span> <span class="nb">int</span> <span class="n">call_function</span> <span class="o">&lt;</span><span class="n">function</span> <span class="n">sym_min</span> <span class="n">at</span> <span class="mh">0x7ff65abf64c0</span><span class="o">&gt;</span>
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 </div>
 </div>
diff --git a/main/generated/torch.Event.html b/main/generated/torch.Event.html
index 35aaad9ed81..0229d1c7360 100644
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@@ -300,7 +300,7 @@
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Generator.html b/main/generated/torch.Generator.html
index f137c4ad0c4..803482778f6 100644
--- a/main/generated/torch.Generator.html
+++ b/main/generated/torch.Generator.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Stream.html b/main/generated/torch.Stream.html
index 5b9f6cc317b..3e5b54dc57e 100644
--- a/main/generated/torch.Stream.html
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@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.abs.html b/main/generated/torch.Tensor.abs.html
index 763ef569011..f3283a81610 100644
--- a/main/generated/torch.Tensor.abs.html
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@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.abs_.html b/main/generated/torch.Tensor.abs_.html
index 06780de994a..dd23f678dfb 100644
--- a/main/generated/torch.Tensor.abs_.html
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@@ -300,7 +300,7 @@
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.absolute.html b/main/generated/torch.Tensor.absolute.html
index a4ad28af4f9..7b47e729bab 100644
--- a/main/generated/torch.Tensor.absolute.html
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@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/generated/torch.Tensor.absolute_.html b/main/generated/torch.Tensor.absolute_.html
index c763ac9229f..e8c8ba313b3 100644
--- a/main/generated/torch.Tensor.absolute_.html
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@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.acos.html b/main/generated/torch.Tensor.acos.html
index e0810eb9a0d..ca1176eb874 100644
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.acos_.html b/main/generated/torch.Tensor.acos_.html
index 9b5a1976e17..bb1c7c9f992 100644
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@@ -300,7 +300,7 @@
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     <div class="version">
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.acosh.html b/main/generated/torch.Tensor.acosh.html
index 54ccaf8befd..5cc5058b03c 100644
--- a/main/generated/torch.Tensor.acosh.html
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@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.acosh_.html b/main/generated/torch.Tensor.acosh_.html
index 1279a930ffb..ab43f83aacc 100644
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@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.add.html b/main/generated/torch.Tensor.add.html
index c006fb9b923..404e220558d 100644
--- a/main/generated/torch.Tensor.add.html
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@@ -300,7 +300,7 @@
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diff --git a/main/generated/torch.Tensor.add_.html b/main/generated/torch.Tensor.add_.html
index 24556d81d0e..c0724ae0450 100644
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     <div class="version">
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addbmm.html b/main/generated/torch.Tensor.addbmm.html
index 9ace60b7be2..c7fe769948b 100644
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addbmm_.html b/main/generated/torch.Tensor.addbmm_.html
index 5eb3e5a3825..d4a06f48aae 100644
--- a/main/generated/torch.Tensor.addbmm_.html
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@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addcdiv.html b/main/generated/torch.Tensor.addcdiv.html
index 9988ec132f0..6d359162e1c 100644
--- a/main/generated/torch.Tensor.addcdiv.html
+++ b/main/generated/torch.Tensor.addcdiv.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addcdiv_.html b/main/generated/torch.Tensor.addcdiv_.html
index 9a90c453b67..79b532f538e 100644
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@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addcmul.html b/main/generated/torch.Tensor.addcmul.html
index fa23b01cd9a..561e317a7ab 100644
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@@ -300,7 +300,7 @@
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 <h1>torch.Tensor.backward<a class="headerlink" href="#torch-tensor-backward" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.backward">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the gradient of current tensor wrt graph leaves.</p>
 <p>The graph is differentiated using the chain rule. If the tensor is
 non-scalar (i.e. its data has more than one element) and requires
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@@ -612,10 +612,10 @@
 <h1>torch.Tensor.dim_order<a class="headerlink" href="#torch-tensor-dim-order" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.dim_order">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">dim_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ambiguity_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.dim_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.dim_order" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">dim_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ambiguity_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.dim_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.dim_order" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the uniquely determined tuple of int describing the dim order or
 physical layout of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>.</p>
-<p>The dim order represents how dimensions are laid out in memory,
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 starting from the outermost to the innermost dimension.</p>
 <p>Note that the dim order may not always be uniquely determined.
 If <cite>ambiguity_check</cite> is True, this function raises a RuntimeError when the dim order cannot be uniquely determined;
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 <h1>torch.Tensor.is_shared<a class="headerlink" href="#torch-tensor-is-shared" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.is_shared">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">is_shared</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.is_shared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L821"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.is_shared" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">is_shared</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.is_shared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L821"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.is_shared" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checks if tensor is in shared memory.</p>
 <p>This is always <code class="docutils literal notranslate"><span class="pre">True</span></code> for CUDA tensors.</p>
 </dd></dl>
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 <h1>torch.Tensor.istft<a class="headerlink" href="#torch-tensor-istft" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.istft">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">istft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.istft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L978"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.istft" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">istft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.istft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L978"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.istft" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.istft.html#torch.istft" title="torch.istft"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.istft()</span></code></a></p>
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 <h1>torch.Tensor.lu<a class="headerlink" href="#torch-tensor-lu" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">lu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pivot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">get_infos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.lu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L916"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.lu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">lu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pivot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">get_infos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.lu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L916"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.lu" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.lu.html#torch.lu" title="torch.lu"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.lu()</span></code></a></p>
 </dd></dl>
 
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diff --git a/main/generated/torch.Tensor.module_load.html b/main/generated/torch.Tensor.module_load.html
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@@ -612,7 +612,7 @@
 <h1>torch.Tensor.module_load<a class="headerlink" href="#torch-tensor-module-load" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.module_load">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">module_load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.module_load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L843"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.module_load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">module_load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.module_load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L843"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.module_load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Defines how to transform <code class="docutils literal notranslate"><span class="pre">other</span></code> when loading it into <code class="docutils literal notranslate"><span class="pre">self</span></code> in <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.load_state_dict" title="torch.nn.Module.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a>.</p>
 <p>Used when <a class="reference internal" href="../future_mod.html#torch.__future__.get_swap_module_params_on_conversion" title="torch.__future__.get_swap_module_params_on_conversion"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_swap_module_params_on_conversion()</span></code></a> is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <p>It is expected that <code class="docutils literal notranslate"><span class="pre">self</span></code> is a parameter or buffer in an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> and <code class="docutils literal notranslate"><span class="pre">other</span></code> is the
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 <h1>torch.Tensor.norm<a class="headerlink" href="#torch-tensor-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.norm">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.norm.html#torch.norm" title="torch.norm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.norm()</span></code></a></p>
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 <h1>torch.Tensor.register_hook<a class="headerlink" href="#torch-tensor-register-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
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-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">register_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.register_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.register_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">register_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.register_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.register_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a backward hook.</p>
 <p>The hook will be called every time a gradient with respect to the
 Tensor is computed. The hook should have the following signature:</p>
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 <h1>torch.Tensor.register_post_accumulate_grad_hook<a class="headerlink" href="#torch-tensor-register-post-accumulate-grad-hook" title="Permalink to this heading">¶</a></h1>
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+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">register_post_accumulate_grad_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.register_post_accumulate_grad_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L702"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.register_post_accumulate_grad_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a backward hook that runs after grad accumulation.</p>
 <p>The hook will be called after all gradients for a tensor have been accumulated,
 meaning that the .grad field has been updated on that tensor. The post
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 <h1>torch.Tensor.share_memory_<a class="headerlink" href="#torch-tensor-share-memory" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.share_memory_">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.share_memory_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.share_memory_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Moves the underlying storage to shared memory.</p>
 <p>This is a no-op if the underlying storage is already in shared memory
 and for CUDA tensors. Tensors in shared memory cannot be resized.</p>
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diff --git a/main/generated/torch.Tensor.sparse_resize_and_clear_.html b/main/generated/torch.Tensor.sparse_resize_and_clear_.html
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diff --git a/main/generated/torch.Tensor.split.html b/main/generated/torch.Tensor.split.html
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@@ -612,7 +612,7 @@
 <h1>torch.Tensor.split<a class="headerlink" href="#torch-tensor-split" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.split">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.split" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.split" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.split.html#torch.split" title="torch.split"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.split()</span></code></a></p>
 </dd></dl>
 
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diff --git a/main/generated/torch.Tensor.stft.html b/main/generated/torch.Tensor.stft.html
index 17b9405d04c..13dca69f84e 100644
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@@ -612,7 +612,7 @@
 <h1>torch.Tensor.stft<a class="headerlink" href="#torch-tensor-stft" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.Tensor.stft">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">stft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.stft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.stft" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">stft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.stft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.stft" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.stft.html#torch.stft" title="torch.stft"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.stft()</span></code></a></p>
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 <h1>torch.Tensor.storage<a class="headerlink" href="#torch-tensor-storage" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.storage">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">storage</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../storage.html#torch.TypedStorage" title="torch.TypedStorage"><span class="pre">torch.TypedStorage</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.storage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">storage</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../storage.html#torch.TypedStorage" title="torch.TypedStorage"><span class="pre">torch.TypedStorage</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.storage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the underlying <a class="reference internal" href="../storage.html#torch.TypedStorage" title="torch.TypedStorage"><code class="xref py py-class docutils literal notranslate"><span class="pre">TypedStorage</span></code></a>.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.Tensor.storage_type<a class="headerlink" href="#torch-tensor-storage-type" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.storage_type">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">storage_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.storage_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.storage_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">storage_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.storage_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.storage_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the type of the underlying storage.</p>
 </dd></dl>
 
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 <h1>torch.Tensor.to_sparse_coo<a class="headerlink" href="#torch-tensor-to-sparse-coo" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.to_sparse_coo">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">to_sparse_coo</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.to_sparse_coo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.to_sparse_coo" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">to_sparse_coo</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.to_sparse_coo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.to_sparse_coo" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a tensor to <a class="reference internal" href="../sparse.html#sparse-coo-docs"><span class="std std-ref">coordinate format</span></a>.</p>
 <p>Examples:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
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diff --git a/main/generated/torch.Tensor.unflatten.html b/main/generated/torch.Tensor.unflatten.html
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 <h1>torch.Tensor.unflatten<a class="headerlink" href="#torch-tensor-unflatten" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.unflatten">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sizes</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unflatten" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sizes</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unflatten" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.unflatten.html#torch.unflatten" title="torch.unflatten"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.unflatten()</span></code></a>.</p>
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diff --git a/main/generated/torch.Tensor.unique.html b/main/generated/torch.Tensor.unique.html
index cf3933dc4fc..0b5fc837384 100644
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 <h1>torch.Tensor.unique<a class="headerlink" href="#torch-tensor-unique" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.unique">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unique</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unique"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1052"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unique" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unique</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unique"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1052"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unique" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the unique elements of the input tensor.</p>
 <p>See <a class="reference internal" href="torch.unique.html#torch.unique" title="torch.unique"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.unique()</span></code></a></p>
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diff --git a/main/generated/torch.Tensor.unique_consecutive.html b/main/generated/torch.Tensor.unique_consecutive.html
index 1895dc4b479..e740729e39e 100644
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 <h1>torch.Tensor.unique_consecutive<a class="headerlink" href="#torch-tensor-unique-consecutive" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.unique_consecutive">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unique_consecutive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unique_consecutive"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1075"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unique_consecutive" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unique_consecutive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unique_consecutive"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1075"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unique_consecutive" title="Permalink to this definition">¶</a></dt>
 <dd><p>Eliminates all but the first element from every consecutive group of equivalent elements.</p>
 <p>See <a class="reference internal" href="torch.unique_consecutive.html#torch.unique_consecutive" title="torch.unique_consecutive"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.unique_consecutive()</span></code></a></p>
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@@ -612,7 +612,7 @@
 <h1>torch._assert<a class="headerlink" href="#torch-assert" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch._assert">
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+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">_assert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">condition</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#_assert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L2134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._assert" title="Permalink to this definition">¶</a></dt>
 <dd><p>A wrapper around Python’s assert which is symbolically traceable.</p>
 </dd></dl>
 
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@@ -616,7 +616,7 @@
 <h1>torch._logging.set_logs<a class="headerlink" href="#torch-logging-set-logs" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch._logging.set_logs">
-<span class="sig-prename descclassname"><span class="pre">torch._logging.</span></span><span class="sig-name descname"><span class="pre">set_logs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autograd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inductor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distributed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c10d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fsdp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bytecode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot_joint_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddp_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_breaks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompiles_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_bytecode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perf_hints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_grad_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_grad_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx_diagnostics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fusion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudagraphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compiled_autograd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compiled_autograd_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudagraph_static_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">benchmarking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_region_expansion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_logging/_internal.html#set_logs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_logging/_internal.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._logging.set_logs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch._logging.</span></span><span class="sig-name descname"><span class="pre">set_logs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autograd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inductor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distributed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c10d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fsdp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bytecode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot_joint_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddp_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_breaks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompiles_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_bytecode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perf_hints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_grad_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_grad_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx_diagnostics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fusion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudagraphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compiled_autograd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compiled_autograd_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudagraph_static_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">benchmarking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_region_expansion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_logging/_internal.html#set_logs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_logging/_internal.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._logging.set_logs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the log level for individual components and toggles individual log
 artifact types.</p>
 <div class="admonition warning">
diff --git a/main/generated/torch.abs.html b/main/generated/torch.abs.html
index ef44bcc5b6f..6595d8057fb 100644
--- a/main/generated/torch.abs.html
+++ b/main/generated/torch.abs.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.absolute.html b/main/generated/torch.absolute.html
index df86050d8d5..1de65b82a75 100644
--- a/main/generated/torch.absolute.html
+++ b/main/generated/torch.absolute.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.accelerator.current_accelerator.html b/main/generated/torch.accelerator.current_accelerator.html
index ddd6374dab4..56cea5467d3 100644
--- a/main/generated/torch.accelerator.current_accelerator.html
+++ b/main/generated/torch.accelerator.current_accelerator.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.current_accelerator<a class="headerlink" href="#torch-accelerator-current-accelerator" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.current_accelerator">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_accelerator</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_accelerator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_accelerator" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_accelerator</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_accelerator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_accelerator" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the device of the current <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.current_device_idx.html b/main/generated/torch.accelerator.current_device_idx.html
index c40fb959c6d..4bf6e59b3ec 100644
--- a/main/generated/torch.accelerator.current_device_idx.html
+++ b/main/generated/torch.accelerator.current_device_idx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.current_device_idx<a class="headerlink" href="#torch-accelerator-current-device-idx" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.current_device_idx">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_device_idx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_device_idx" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_device_idx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_device_idx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device for the current <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.current_device_index.html b/main/generated/torch.accelerator.current_device_index.html
index 25889f0c3b7..0d3bdb6a682 100644
--- a/main/generated/torch.accelerator.current_device_index.html
+++ b/main/generated/torch.accelerator.current_device_index.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.accelerator.current_device_index<a class="headerlink" href="#torch-accelerator-current-device-index" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.current_device_index">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_device_index</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_device_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_device_index" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_device_index</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_device_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_device_index" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device for the current <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.current_stream.html b/main/generated/torch.accelerator.current_stream.html
index aa0d3b1491e..513dfdc7329 100644
--- a/main/generated/torch.accelerator.current_stream.html
+++ b/main/generated/torch.accelerator.current_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.current_stream<a class="headerlink" href="#torch-accelerator-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the currently selected stream for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.accelerator.device_count.html b/main/generated/torch.accelerator.device_count.html
index a38c2dfedb7..4fb3c0ca6a6 100644
--- a/main/generated/torch.accelerator.device_count.html
+++ b/main/generated/torch.accelerator.device_count.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.device_count<a class="headerlink" href="#torch-accelerator-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of current <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a> available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.is_available.html b/main/generated/torch.accelerator.is_available.html
index 5836ecd01a4..49e65056bba 100644
--- a/main/generated/torch.accelerator.is_available.html
+++ b/main/generated/torch.accelerator.is_available.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.is_available<a class="headerlink" href="#torch-accelerator-is-available" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if there is an available <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.set_device_idx.html b/main/generated/torch.accelerator.set_device_idx.html
index a29e7871577..c97addf45d7 100644
--- a/main/generated/torch.accelerator.set_device_idx.html
+++ b/main/generated/torch.accelerator.set_device_idx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.set_device_idx<a class="headerlink" href="#torch-accelerator-set-device-idx" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.set_device_idx">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_device_idx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_device_idx" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_device_idx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_device_idx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device index to a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.accelerator.set_device_index.html b/main/generated/torch.accelerator.set_device_index.html
index 5989d4888d8..9ad6e47f183 100644
--- a/main/generated/torch.accelerator.set_device_index.html
+++ b/main/generated/torch.accelerator.set_device_index.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.set_device_index<a class="headerlink" href="#torch-accelerator-set-device-index" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.set_device_index">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_device_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#set_device_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_device_index" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_device_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#set_device_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_device_index" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device index to a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.accelerator.set_stream.html b/main/generated/torch.accelerator.set_stream.html
index cbea3a11bca..72286b5c511 100644
--- a/main/generated/torch.accelerator.set_stream.html
+++ b/main/generated/torch.accelerator.set_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.set_stream<a class="headerlink" href="#torch-accelerator-set-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.set_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current stream to a given stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.accelerator.synchronize.html b/main/generated/torch.accelerator.synchronize.html
index 8aee6a4ec22..38e52b505ed 100644
--- a/main/generated/torch.accelerator.synchronize.html
+++ b/main/generated/torch.accelerator.synchronize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.synchronize<a class="headerlink" href="#torch-accelerator-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/accelerator/__init__.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all kernels in all streams on the given device to complete.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
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index a5c99f954df..6fe87a3032e 100644
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diff --git a/main/generated/torch.ao.nn.intrinsic.BNReLU2d.html b/main/generated/torch.ao.nn.intrinsic.BNReLU2d.html
index 9e8c15c94f7..6f9cc6011a6 100644
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@@ -614,7 +614,7 @@
 <h1>BNReLU2d<a class="headerlink" href="#bnrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.BNReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">BNReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#BNReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.BNReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">BNReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#BNReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.BNReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the BatchNorm 2d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.BNReLU3d.html b/main/generated/torch.ao.nn.intrinsic.BNReLU3d.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -614,7 +614,7 @@
 <h1>BNReLU3d<a class="headerlink" href="#bnrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.BNReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">BNReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#BNReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.BNReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">BNReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#BNReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.BNReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the BatchNorm 3d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBn1d.html b/main/generated/torch.ao.nn.intrinsic.ConvBn1d.html
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 <h1>ConvBn1d<a class="headerlink" href="#convbn1d" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBn1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 1d and Batch Norm 1d modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBn2d.html b/main/generated/torch.ao.nn.intrinsic.ConvBn2d.html
index bcecf233563..e792d5eb120 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBn2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBn2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn2d<a class="headerlink" href="#convbn2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBn2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 2d and Batch Norm 2d modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBn3d.html b/main/generated/torch.ao.nn.intrinsic.ConvBn3d.html
index d960b0df6c7..8694d514f02 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBn3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBn3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn3d<a class="headerlink" href="#convbn3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBn3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 3d and Batch Norm 3d modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html
index d7b314a4fda..5ba999316d1 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU1d<a class="headerlink" href="#convbnrelu1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBnReLU1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 1d, Batch Norm 1d, and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html
index 336fda0fad9..b79b25985d5 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU2d<a class="headerlink" href="#convbnrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBnReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 2d, Batch Norm 2d, and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html
index abc1eef389f..acaee729e59 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU3d<a class="headerlink" href="#convbnrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBnReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 3d, Batch Norm 3d, and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvReLU1d.html b/main/generated/torch.ao.nn.intrinsic.ConvReLU1d.html
index 79d20a597c7..8b26d3da8c6 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvReLU1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvReLU1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU1d<a class="headerlink" href="#convrelu1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvReLU1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv1d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvReLU2d.html b/main/generated/torch.ao.nn.intrinsic.ConvReLU2d.html
index 5268cb63cfd..6914628ef1d 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU2d<a class="headerlink" href="#convrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv2d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvReLU3d.html b/main/generated/torch.ao.nn.intrinsic.ConvReLU3d.html
index 208db841bac..40666aeb4ae 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>ConvReLU3d<a class="headerlink" href="#convrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv3d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.LinearReLU.html b/main/generated/torch.ao.nn.intrinsic.LinearReLU.html
index f8fcfb87bbc..364d2af2ef9 100644
--- a/main/generated/torch.ao.nn.intrinsic.LinearReLU.html
+++ b/main/generated/torch.ao.nn.intrinsic.LinearReLU.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LinearReLU<a class="headerlink" href="#linearrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.LinearReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/modules/fused.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.LinearReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.LinearReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Linear and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html
index 5d0bcee0384..5b4a8f5dd3f 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn1d<a class="headerlink" href="#convbn1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBn1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBn1d module is a module fused from Conv1d and BatchNorm1d,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html
index 7b3c14b0ea7..6643c233978 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn2d<a class="headerlink" href="#convbn2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBn2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBn2d module is a module fused from Conv2d and BatchNorm2d,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html
index c9def7d5fcb..7db26aba97b 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn3d<a class="headerlink" href="#convbn3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBn3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBn3d module is a module fused from Conv3d and BatchNorm3d,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html
index e1b86b8df36..07c047f9649 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU1d<a class="headerlink" href="#convbnrelu1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBnReLU1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBnReLU1d module is a module fused from Conv1d, BatchNorm1d and ReLU,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU2d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU2d.html
index 25644c64b01..1855a45ec00 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU2d<a class="headerlink" href="#convbnrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBnReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html
index da2c5012771..3129905b838 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU3d<a class="headerlink" href="#convbnrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBnReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L905"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L905"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBnReLU3d module is a module fused from Conv3d, BatchNorm3d and ReLU,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html
index fb16c30ac44..11bff8def70 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU2d<a class="headerlink" href="#convrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L778"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L778"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with
 FakeQuantize modules for weight for
 quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html
index 4fc6c0e44ff..08a5e867b71 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU3d<a class="headerlink" href="#convrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L976"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L976"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU3d module is a fused module of Conv3d and ReLU, attached with
 FakeQuantize modules for weight for
 quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.LinearReLU.html b/main/generated/torch.ao.nn.intrinsic.qat.LinearReLU.html
index 20dbe96e78d..a387ddaaf73 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.LinearReLU.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.LinearReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LinearReLU<a class="headerlink" href="#linearrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.LinearReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/linear_relu.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.LinearReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/linear_relu.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.LinearReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>A LinearReLU module fused from Linear and ReLU modules, attached with
 FakeQuantize modules for weight, used in
 quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html b/main/generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html
index 7ee77c00ce2..9f558309f5c 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>freeze_bn_stats<a class="headerlink" href="#freeze-bn-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.freeze_bn_stats">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">freeze_bn_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#freeze_bn_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.freeze_bn_stats" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">freeze_bn_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#freeze_bn_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.freeze_bn_stats" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html b/main/generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html
index 7b81c49510c..70eb3e1d3b3 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>update_bn_stats<a class="headerlink" href="#update-bn-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.update_bn_stats">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">update_bn_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#update_bn_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L1034"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.update_bn_stats" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">update_bn_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#update_bn_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L1034"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.update_bn_stats" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html b/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html
index 95a5ba6c087..d71b3b2ab9b 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BNReLU2d<a class="headerlink" href="#bnrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.BNReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">BNReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/bn_relu.html#BNReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.BNReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">BNReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/bn_relu.html#BNReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.BNReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A BNReLU2d module is a fused module of BatchNorm2d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.BatchNorm2d.html#torch.ao.nn.quantized.BatchNorm2d" title="torch.ao.nn.quantized.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.BatchNorm2d</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html b/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html
index b60f10da736..53a8728cfcf 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BNReLU3d<a class="headerlink" href="#bnrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.BNReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">BNReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/bn_relu.html#BNReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.BNReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">BNReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/bn_relu.html#BNReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.BNReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A BNReLU3d module is a fused module of BatchNorm3d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.BatchNorm3d.html#torch.ao.nn.quantized.BatchNorm3d" title="torch.ao.nn.quantized.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.BatchNorm3d</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html
index a49fd7a92e3..38da02de0d7 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU1d<a class="headerlink" href="#convrelu1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.ConvReLU1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU1d module is a fused module of Conv1d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.Conv1d.html#torch.ao.nn.quantized.Conv1d" title="torch.ao.nn.quantized.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.Conv1d</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html
index d0766e596e7..2f768822fa0 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU2d<a class="headerlink" href="#convrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.ConvReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU2d module is a fused module of Conv2d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.Conv2d.html#torch.ao.nn.quantized.Conv2d" title="torch.ao.nn.quantized.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.Conv2d</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html
index d4c3fb1c69e..f51c87c3025 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU3d<a class="headerlink" href="#convrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.ConvReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU3d module is a fused module of Conv3d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.Conv3d.html#torch.ao.nn.quantized.Conv3d" title="torch.ao.nn.quantized.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.Conv3d</span></code></a>.</p>
 <p>Attributes: Same as torch.ao.nn.quantized.Conv3d</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html b/main/generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html
index 3f756cdaa1b..3c6dcd491ef 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LinearReLU<a class="headerlink" href="#linearrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.LinearReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.LinearReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.LinearReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>A LinearReLU module fused from Linear and ReLU modules</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.Linear.html#torch.ao.nn.quantized.Linear" title="torch.ao.nn.quantized.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.Linear</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html b/main/generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html
index 32cbc4e1c55..474c6c317f4 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LinearReLU<a class="headerlink" href="#linearrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>A LinearReLU module fused from Linear and ReLU modules that can be used
 for dynamic quantization.
 Supports both, FP16 and INT8 quantization.</p>
diff --git a/main/generated/torch.ao.nn.qat.Conv2d.html b/main/generated/torch.ao.nn.qat.Conv2d.html
index 10a4e211c82..20644550897 100644
--- a/main/generated/torch.ao.nn.qat.Conv2d.html
+++ b/main/generated/torch.ao.nn.qat.Conv2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.Conv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/qat/modules/conv.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Conv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/modules/conv.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Conv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A Conv2d module attached with FakeQuantize modules for weight,
 used for quantization aware training.</p>
 <p>We adopt the same interface as <cite>torch.nn.Conv2d</cite>, please see
diff --git a/main/generated/torch.ao.nn.qat.Conv3d.html b/main/generated/torch.ao.nn.qat.Conv3d.html
index e51d8ee3bad..e6bc59d7d88 100644
--- a/main/generated/torch.ao.nn.qat.Conv3d.html
+++ b/main/generated/torch.ao.nn.qat.Conv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.Conv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/qat/modules/conv.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Conv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/modules/conv.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Conv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A Conv3d module attached with FakeQuantize modules for weight,
 used for quantization aware training.</p>
 <p>We adopt the same interface as <cite>torch.nn.Conv3d</cite>, please see
diff --git a/main/generated/torch.ao.nn.qat.Linear.html b/main/generated/torch.ao.nn.qat.Linear.html
index c12cdbe24a8..ce721cdec92 100644
--- a/main/generated/torch.ao.nn.qat.Linear.html
+++ b/main/generated/torch.ao.nn.qat.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/qat/modules/linear.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/modules/linear.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A linear module attached with FakeQuantize modules for weight,
 used for quantization aware training.</p>
 <p>We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
@@ -629,7 +629,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.Linear.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/qat/modules/linear.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Linear.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/modules/linear.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Linear.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a qat module from a float module or qparams_dict
 Args: <cite>mod</cite> a float module, either produced by torch.ao.quantization utilities
 or directly from user</p>
diff --git a/main/generated/torch.ao.nn.qat.dynamic.Linear.html b/main/generated/torch.ao.nn.qat.dynamic.Linear.html
index 4a9dd2289e9..64f0299014e 100644
--- a/main/generated/torch.ao.nn.qat.dynamic.Linear.html
+++ b/main/generated/torch.ao.nn.qat.dynamic.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.dynamic.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.dynamic.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/dynamic/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/qat/dynamic/modules/linear.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.dynamic.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.dynamic.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/dynamic/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/dynamic/modules/linear.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.dynamic.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A linear module attached with FakeQuantize modules for weight,
 used for dynamic quantization aware training.</p>
 <p>We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
diff --git a/main/generated/torch.ao.nn.quantizable.LSTM.html b/main/generated/torch.ao.nn.quantizable.LSTM.html
index a0da63308fa..9c1d3e85ee7 100644
--- a/main/generated/torch.ao.nn.quantizable.LSTM.html
+++ b/main/generated/torch.ao.nn.quantizable.LSTM.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LSTM<a class="headerlink" href="#lstm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantizable.LSTM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantizable.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_gates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantizable/modules/rnn.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.LSTM" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantizable.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_gates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantizable/modules/rnn.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.LSTM" title="Permalink to this definition">¶</a></dt>
 <dd><p>A quantizable long short-term memory (LSTM).</p>
 <p>For the description and the argument types, please, refer to <a class="reference internal" href="torch.nn.LSTM.html#torch.nn.LSTM" title="torch.nn.LSTM"><code class="xref py py-class docutils literal notranslate"><span class="pre">LSTM</span></code></a></p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.quantizable.MultiheadAttention.html b/main/generated/torch.ao.nn.quantizable.MultiheadAttention.html
index d3c63841a9b..c3887b3493c 100644
--- a/main/generated/torch.ao.nn.quantizable.MultiheadAttention.html
+++ b/main/generated/torch.ao.nn.quantizable.MultiheadAttention.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,12 +614,12 @@
 <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantizable.MultiheadAttention">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantizable.</span></span><span class="sig-name descname"><span class="pre">MultiheadAttention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_bias_kv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_zero_attn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantizable/modules/activation.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantizable.</span></span><span class="sig-name descname"><span class="pre">MultiheadAttention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_bias_kv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_zero_attn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantizable/modules/activation.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantizable.MultiheadAttention.dequantize">
-<span class="sig-name descname"><span class="pre">dequantize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention.dequantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantizable/modules/activation.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention.dequantize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">dequantize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention.dequantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantizable/modules/activation.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention.dequantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to convert the quantized MHA back to float.</p>
 <p>The motivation for this is that it is not trivial to conver the weights
 from the format that is used in the quantized version back to the
@@ -628,7 +628,7 @@ <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantizable.MultiheadAttention.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">need_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_attn_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantizable/modules/activation.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">need_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_attn_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantizable/modules/activation.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention.forward" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Note::</dt><dd><p>Please, refer to <a class="reference internal" href="torch.nn.MultiheadAttention.html#torch.nn.MultiheadAttention.forward" title="torch.nn.MultiheadAttention.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> for more
 information</p>
diff --git a/main/generated/torch.ao.nn.quantized.BatchNorm2d.html b/main/generated/torch.ao.nn.quantized.BatchNorm2d.html
index e9296df5a34..8d25c0ad117 100644
--- a/main/generated/torch.ao.nn.quantized.BatchNorm2d.html
+++ b/main/generated/torch.ao.nn.quantized.BatchNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BatchNorm2d<a class="headerlink" href="#batchnorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.BatchNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">BatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/batchnorm.html#BatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/batchnorm.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.BatchNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">BatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/batchnorm.html#BatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/batchnorm.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.BatchNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.ao.nn.quantized.BatchNorm3d.html b/main/generated/torch.ao.nn.quantized.BatchNorm3d.html
index 355cc8d5081..43221a1e0ff 100644
--- a/main/generated/torch.ao.nn.quantized.BatchNorm3d.html
+++ b/main/generated/torch.ao.nn.quantized.BatchNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BatchNorm3d<a class="headerlink" href="#batchnorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.BatchNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">BatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/batchnorm.html#BatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/batchnorm.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.BatchNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">BatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/batchnorm.html#BatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/batchnorm.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.BatchNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm3d</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.ao.nn.quantized.Conv1d.html b/main/generated/torch.ao.nn.quantized.Conv1d.html
index d36d4ef4d93..83bc5c12366 100644
--- a/main/generated/torch.ao.nn.quantized.Conv1d.html
+++ b/main/generated/torch.ao.nn.quantized.Conv1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv1d<a class="headerlink" href="#conv1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/conv.py#L353"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L353"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D convolution over a quantized input signal composed of
 several quantized input planes.</p>
 <p>For details on input arguments, parameters, and implementation see
@@ -649,7 +649,7 @@ <h1>Conv1d<a class="headerlink" href="#conv1d" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv1d.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv1d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/conv.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv1d.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv1d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv1d.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a quantized module from a float module or qparams_dict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.Conv2d.html b/main/generated/torch.ao.nn.quantized.Conv2d.html
index 2c0e3e01cb7..e72b83ec0e2 100644
--- a/main/generated/torch.ao.nn.quantized.Conv2d.html
+++ b/main/generated/torch.ao.nn.quantized.Conv2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/conv.py#L482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D convolution over a quantized input signal composed of
 several quantized input planes.</p>
 <p>For details on input arguments, parameters, and implementation see
@@ -653,7 +653,7 @@ <h1>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv2d.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv2d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/conv.py#L598"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv2d.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv2d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L598"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv2d.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a quantized module from a float module or qparams_dict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.Conv3d.html b/main/generated/torch.ao.nn.quantized.Conv3d.html
index 6a3444975eb..068386825e7 100644
--- a/main/generated/torch.ao.nn.quantized.Conv3d.html
+++ b/main/generated/torch.ao.nn.quantized.Conv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/conv.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D convolution over a quantized input signal composed of
 several quantized input planes.</p>
 <p>For details on input arguments, parameters, and implementation see
@@ -653,7 +653,7 @@ <h1>Conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv3d.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv3d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/conv.py#L728"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv3d.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv3d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L728"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv3d.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a quantized module from a float module or qparams_dict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.ConvTranspose1d.html b/main/generated/torch.ao.nn.quantized.ConvTranspose1d.html
index 4db25c6764e..06c4537cdb3 100644
--- a/main/generated/torch.ao.nn.quantized.ConvTranspose1d.html
+++ b/main/generated/torch.ao.nn.quantized.ConvTranspose1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvTranspose1d<a class="headerlink" href="#convtranspose1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ConvTranspose1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/conv.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D transposed convolution operator over an input image
 composed of several input planes.
 For details on input arguments, parameters, and implementation see
diff --git a/main/generated/torch.ao.nn.quantized.ConvTranspose2d.html b/main/generated/torch.ao.nn.quantized.ConvTranspose2d.html
index 1ac31c9090c..ab19e347aa4 100644
--- a/main/generated/torch.ao.nn.quantized.ConvTranspose2d.html
+++ b/main/generated/torch.ao.nn.quantized.ConvTranspose2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvTranspose2d<a class="headerlink" href="#convtranspose2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ConvTranspose2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/conv.py#L996"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L996"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D transposed convolution operator over an input image
 composed of several input planes.
 For details on input arguments, parameters, and implementation see
diff --git a/main/generated/torch.ao.nn.quantized.ConvTranspose3d.html b/main/generated/torch.ao.nn.quantized.ConvTranspose3d.html
index b705f96e7ca..5fa8d41b86e 100644
--- a/main/generated/torch.ao.nn.quantized.ConvTranspose3d.html
+++ b/main/generated/torch.ao.nn.quantized.ConvTranspose3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvTranspose3d<a class="headerlink" href="#convtranspose3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ConvTranspose3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/conv.py#L1119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L1119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D transposed convolution operator over an input image
 composed of several input planes.
 For details on input arguments, parameters, and implementation see
diff --git a/main/generated/torch.ao.nn.quantized.ELU.html b/main/generated/torch.ao.nn.quantized.ELU.html
index 00c34b4cbdd..8da433a27d2 100644
--- a/main/generated/torch.ao.nn.quantized.ELU.html
+++ b/main/generated/torch.ao.nn.quantized.ELU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ELU<a class="headerlink" href="#elu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#ELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/activation.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#ELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized equivalent of <a class="reference internal" href="torch.nn.ELU.html#torch.nn.ELU" title="torch.nn.ELU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ELU</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.Embedding.html b/main/generated/torch.ao.nn.quantized.Embedding.html
index ad98acd83a3..fb4f887f0ad 100644
--- a/main/generated/torch.ao.nn.quantized.Embedding.html
+++ b/main/generated/torch.ao.nn.quantized.Embedding.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Embedding<a class="headerlink" href="#embedding" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Embedding">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#Embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/embedding_ops.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Embedding" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#Embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/embedding_ops.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Embedding" title="Permalink to this definition">¶</a></dt>
 <dd><p>A quantized Embedding module with quantized packed weights as inputs.
 We adopt the same interface as <cite>torch.nn.Embedding</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html">https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html</a> for documentation.</p>
@@ -638,7 +638,7 @@ <h1>Embedding<a class="headerlink" href="#embedding" title="Permalink to this he
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Embedding.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#Embedding.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/embedding_ops.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Embedding.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#Embedding.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/embedding_ops.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Embedding.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a quantized embedding module from a float module</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.EmbeddingBag.html b/main/generated/torch.ao.nn.quantized.EmbeddingBag.html
index b13a44796db..71da5c0fdd1 100644
--- a/main/generated/torch.ao.nn.quantized.EmbeddingBag.html
+++ b/main/generated/torch.ao.nn.quantized.EmbeddingBag.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.EmbeddingBag">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">EmbeddingBag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#EmbeddingBag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/embedding_ops.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.EmbeddingBag" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">EmbeddingBag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#EmbeddingBag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/embedding_ops.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.EmbeddingBag" title="Permalink to this definition">¶</a></dt>
 <dd><p>A quantized EmbeddingBag module with quantized packed weights as inputs.
 We adopt the same interface as <cite>torch.nn.EmbeddingBag</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html">https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html</a> for documentation.</p>
@@ -639,7 +639,7 @@ <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.EmbeddingBag.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#EmbeddingBag.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/embedding_ops.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.EmbeddingBag.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#EmbeddingBag.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/embedding_ops.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.EmbeddingBag.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a quantized embedding_bag module from a float module</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.FXFloatFunctional.html b/main/generated/torch.ao.nn.quantized.FXFloatFunctional.html
index 21f2bd73e0d..0a37fa27d83 100644
--- a/main/generated/torch.ao.nn.quantized.FXFloatFunctional.html
+++ b/main/generated/torch.ao.nn.quantized.FXFloatFunctional.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>FXFloatFunctional<a class="headerlink" href="#fxfloatfunctional" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.FXFloatFunctional">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">FXFloatFunctional</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#FXFloatFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/functional_modules.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.FXFloatFunctional" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">FXFloatFunctional</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#FXFloatFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/functional_modules.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.FXFloatFunctional" title="Permalink to this definition">¶</a></dt>
 <dd><p>module to replace FloatFunctional module before FX graph mode quantization,
 since activation_post_process will be inserted in top level module directly</p>
 <dl class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.FloatFunctional.html b/main/generated/torch.ao.nn.quantized.FloatFunctional.html
index 16ec3bf9c39..e2d46e4b5fc 100644
--- a/main/generated/torch.ao.nn.quantized.FloatFunctional.html
+++ b/main/generated/torch.ao.nn.quantized.FloatFunctional.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>FloatFunctional<a class="headerlink" href="#floatfunctional" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.FloatFunctional">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">FloatFunctional</span></span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#FloatFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/functional_modules.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.FloatFunctional" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">FloatFunctional</span></span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#FloatFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/functional_modules.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.FloatFunctional" title="Permalink to this definition">¶</a></dt>
 <dd><p>State collector class for float operations.</p>
 <p>The instance of this class can be used instead of the <code class="docutils literal notranslate"><span class="pre">torch.</span></code> prefix for
 some operations. See example usage below.</p>
diff --git a/main/generated/torch.ao.nn.quantized.GroupNorm.html b/main/generated/torch.ao.nn.quantized.GroupNorm.html
index 82aab927a3c..56b64131020 100644
--- a/main/generated/torch.ao.nn.quantized.GroupNorm.html
+++ b/main/generated/torch.ao.nn.quantized.GroupNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>GroupNorm<a class="headerlink" href="#groupnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.GroupNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">GroupNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#GroupNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/normalization.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.GroupNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">GroupNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#GroupNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.GroupNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.GroupNorm.html#torch.nn.GroupNorm" title="torch.nn.GroupNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupNorm</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.Hardswish.html b/main/generated/torch.ao.nn.quantized.Hardswish.html
index f091c22c807..d06fc72a254 100644
--- a/main/generated/torch.ao.nn.quantized.Hardswish.html
+++ b/main/generated/torch.ao.nn.quantized.Hardswish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Hardswish<a class="headerlink" href="#hardswish" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Hardswish">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#Hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/activation.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Hardswish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#Hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Hardswish" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.Hardswish.html#torch.nn.Hardswish" title="torch.nn.Hardswish"><code class="xref py py-class docutils literal notranslate"><span class="pre">Hardswish</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.InstanceNorm1d.html b/main/generated/torch.ao.nn.quantized.InstanceNorm1d.html
index 65fe8ee3d95..ee83e9bfad2 100644
--- a/main/generated/torch.ao.nn.quantized.InstanceNorm1d.html
+++ b/main/generated/torch.ao.nn.quantized.InstanceNorm1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>InstanceNorm1d<a class="headerlink" href="#instancenorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.InstanceNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/normalization.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm1d</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.InstanceNorm2d.html b/main/generated/torch.ao.nn.quantized.InstanceNorm2d.html
index 7bf07ac9a51..dbf68eb6161 100644
--- a/main/generated/torch.ao.nn.quantized.InstanceNorm2d.html
+++ b/main/generated/torch.ao.nn.quantized.InstanceNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>InstanceNorm2d<a class="headerlink" href="#instancenorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.InstanceNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/normalization.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm2d</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.InstanceNorm3d.html b/main/generated/torch.ao.nn.quantized.InstanceNorm3d.html
index 88547ebaf8d..2e2d196c970 100644
--- a/main/generated/torch.ao.nn.quantized.InstanceNorm3d.html
+++ b/main/generated/torch.ao.nn.quantized.InstanceNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>InstanceNorm3d<a class="headerlink" href="#instancenorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.InstanceNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/normalization.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm3d</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.LayerNorm.html b/main/generated/torch.ao.nn.quantized.LayerNorm.html
index 6308618a773..8bff9abdf47 100644
--- a/main/generated/torch.ao.nn.quantized.LayerNorm.html
+++ b/main/generated/torch.ao.nn.quantized.LayerNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LayerNorm<a class="headerlink" href="#layernorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.LayerNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">LayerNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#LayerNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/normalization.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.LayerNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">LayerNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#LayerNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.LayerNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.LayerNorm.html#torch.nn.LayerNorm" title="torch.nn.LayerNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">LayerNorm</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.LeakyReLU.html b/main/generated/torch.ao.nn.quantized.LeakyReLU.html
index 9b5e47cb8da..addafb2d4e0 100644
--- a/main/generated/torch.ao.nn.quantized.LeakyReLU.html
+++ b/main/generated/torch.ao.nn.quantized.LeakyReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LeakyReLU<a class="headerlink" href="#leakyrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.LeakyReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">LeakyReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#LeakyReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/activation.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.LeakyReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">LeakyReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#LeakyReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.LeakyReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized equivalent of <a class="reference internal" href="torch.nn.LeakyReLU.html#torch.nn.LeakyReLU" title="torch.nn.LeakyReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">LeakyReLU</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.Linear.html b/main/generated/torch.ao.nn.quantized.Linear.html
index c1eaf13be87..a7e619e1e1b 100644
--- a/main/generated/torch.ao.nn.quantized.Linear.html
+++ b/main/generated/torch.ao.nn.quantized.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/linear.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/linear.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A quantized linear module with quantized tensor as inputs and outputs.
 We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Linear">https://pytorch.org/docs/stable/nn.html#torch.nn.Linear</a> for documentation.</p>
@@ -643,7 +643,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Linear.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/linear.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/linear.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a quantized module from an observed float module</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -659,7 +659,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Linear.from_reference">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_reference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_qlinear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear.from_reference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/linear.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear.from_reference" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_reference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_qlinear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear.from_reference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/linear.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear.from_reference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a (fbgemm/qnnpack) quantized module from a reference quantized module</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.QFunctional.html b/main/generated/torch.ao.nn.quantized.QFunctional.html
index 657841e3e5a..066a94b9ee7 100644
--- a/main/generated/torch.ao.nn.quantized.QFunctional.html
+++ b/main/generated/torch.ao.nn.quantized.QFunctional.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>QFunctional<a class="headerlink" href="#qfunctional" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.QFunctional">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">QFunctional</span></span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#QFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/functional_modules.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.QFunctional" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">QFunctional</span></span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#QFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/functional_modules.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.QFunctional" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper class for quantized operations.</p>
 <p>The instance of this class can be used instead of the
 <code class="docutils literal notranslate"><span class="pre">torch.ops.quantized</span></code> prefix. See example usage below.</p>
diff --git a/main/generated/torch.ao.nn.quantized.ReLU6.html b/main/generated/torch.ao.nn.quantized.ReLU6.html
index ad23030cf45..a9082f42c59 100644
--- a/main/generated/torch.ao.nn.quantized.ReLU6.html
+++ b/main/generated/torch.ao.nn.quantized.ReLU6.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ReLU6<a class="headerlink" href="#relu6" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ReLU6">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ReLU6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#ReLU6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/activation.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ReLU6" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ReLU6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#ReLU6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ReLU6" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise function:</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU6</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>x</mi><mn>0</mn></msub><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo separator="true">,</mo><mi>q</mi><mo stretchy="false">(</mo><mn>6</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU6}(x) = \min(\max(x_0, x), q(6))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">ReLU6</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mop">max</span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">0</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mopen">(</span><span class="mord">6</span><span class="mclose">))</span></span></span></span></span>, where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>0</mn></msub></mrow><annotation encoding="application/x-tex">x_0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">0</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is the
 zero_point, and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>q</mi><mo stretchy="false">(</mo><mn>6</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">q(6)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mopen">(</span><span class="mord">6</span><span class="mclose">)</span></span></span></span></span> is the quantized representation of number 6.</p>
diff --git a/main/generated/torch.ao.nn.quantized.Sigmoid.html b/main/generated/torch.ao.nn.quantized.Sigmoid.html
index d368729225c..636bd5c9c7d 100644
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>Sigmoid<a class="headerlink" href="#sigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Sigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#Sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/modules/activation.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Sigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#Sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Sigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized equivalent of <a class="reference internal" href="torch.nn.Sigmoid.html#torch.nn.Sigmoid" title="torch.nn.Sigmoid"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sigmoid</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.GRU.html b/main/generated/torch.ao.nn.quantized.dynamic.GRU.html
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>GRU<a class="headerlink" href="#gru" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.GRU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">GRU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#GRU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/dynamic/modules/rnn.py#L683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.GRU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">GRU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#GRU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.GRU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</p>
 <p>For each element in the input sequence, each layer computes the following
 function:</p>
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.GRUCell.html b/main/generated/torch.ao.nn.quantized.dynamic.GRUCell.html
index 9e6475cd109..0daf9beda46 100644
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+++ b/main/generated/torch.ao.nn.quantized.dynamic.GRUCell.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>GRUCell<a class="headerlink" href="#grucell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.GRUCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">GRUCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#GRUCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.GRUCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">GRUCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#GRUCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.GRUCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>A gated recurrent unit (GRU) cell</p>
 <p>A dynamic quantized GRUCell module with floating point tensor as inputs and outputs.
 Weights are quantized to 8 bits. We adopt the same interface as <cite>torch.nn.GRUCell</cite>,
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.LSTM.html b/main/generated/torch.ao.nn.quantized.dynamic.LSTM.html
index 243509bdb1c..e65f41be861 100644
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>LSTM<a class="headerlink" href="#lstm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.LSTM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/dynamic/modules/rnn.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.LSTM" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.LSTM" title="Permalink to this definition">¶</a></dt>
 <dd><p>A dynamic quantized LSTM module with floating point tensor as inputs and outputs.
 We adopt the same interface as <cite>torch.nn.LSTM</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM">https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM</a> for documentation.</p>
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.LSTMCell.html b/main/generated/torch.ao.nn.quantized.dynamic.LSTMCell.html
index a05ac424868..e46861b6ae3 100644
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>LSTMCell<a class="headerlink" href="#lstmcell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.LSTMCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LSTMCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#LSTMCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.LSTMCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LSTMCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#LSTMCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.LSTMCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>A long short-term memory (LSTM) cell.</p>
 <p>A dynamic quantized LSTMCell module with floating point tensor as inputs and outputs.
 Weights are quantized to 8 bits. We adopt the same interface as <cite>torch.nn.LSTMCell</cite>,
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.Linear.html b/main/generated/torch.ao.nn.quantized.dynamic.Linear.html
index 21a345f288e..aba3191bf04 100644
--- a/main/generated/torch.ao.nn.quantized.dynamic.Linear.html
+++ b/main/generated/torch.ao.nn.quantized.dynamic.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/dynamic/modules/linear.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/linear.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A dynamic quantized linear module with floating point tensor as inputs and outputs.
 We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Linear">https://pytorch.org/docs/stable/nn.html#torch.nn.Linear</a> for documentation.</p>
@@ -641,7 +641,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.Linear.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/dynamic/modules/linear.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/linear.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a dynamic quantized module from a float module or qparams_dict</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -653,7 +653,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.Linear.from_reference">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_reference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_qlinear</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear.from_reference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/dynamic/modules/linear.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear.from_reference" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_reference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_qlinear</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear.from_reference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/linear.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear.from_reference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a (fbgemm/qnnpack) dynamic quantized module from a reference quantized
 module
 :param ref_qlinear: a reference quantized  module, either produced by
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.RNNCell.html b/main/generated/torch.ao.nn.quantized.dynamic.RNNCell.html
index 2465cee5733..fd951abac0d 100644
--- a/main/generated/torch.ao.nn.quantized.dynamic.RNNCell.html
+++ b/main/generated/torch.ao.nn.quantized.dynamic.RNNCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>RNNCell<a class="headerlink" href="#rnncell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.RNNCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">RNNCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#RNNCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.RNNCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">RNNCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#RNNCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.RNNCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>An Elman RNN cell with tanh or ReLU non-linearity.
 A dynamic quantized RNNCell module with floating point tensor as inputs and outputs.
 Weights are quantized to 8 bits. We adopt the same interface as <cite>torch.nn.RNNCell</cite>,
diff --git a/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html b/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html
index 0ac0d1c2f3a..72861f25494 100644
--- a/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>adaptive_avg_pool2d<a class="headerlink" href="#adaptive-avg-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.adaptive_avg_pool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#adaptive_avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#adaptive_avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D adaptive average pooling over a quantized input signal composed
 of several quantized input planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html b/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html
index 2e1596344ba..d9e9afb349c 100644
--- a/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>adaptive_avg_pool3d<a class="headerlink" href="#adaptive-avg-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.adaptive_avg_pool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#adaptive_avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.adaptive_avg_pool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#adaptive_avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.adaptive_avg_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D adaptive average pooling over a quantized input signal composed
 of several quantized input planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.ao.nn.quantized.functional.avg_pool2d.html b/main/generated/torch.ao.nn.quantized.functional.avg_pool2d.html
index 4173c3fe078..ef5be25d7c1 100644
--- a/main/generated/torch.ao.nn.quantized.functional.avg_pool2d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.avg_pool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>avg_pool2d<a class="headerlink" href="#avg-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.avg_pool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L43"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.avg_pool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L43"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.avg_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies 2D average-pooling operation in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by step size
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>s</mi><mi>H</mi><mo>×</mo><mi>s</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">sH \times sW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">sH</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">s</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span></span></span></span></span> steps. The number of output features is equal to the number of
 input planes.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.avg_pool3d.html b/main/generated/torch.ao.nn.quantized.functional.avg_pool3d.html
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@@ -614,7 +614,7 @@
 <h1>avg_pool3d<a class="headerlink" href="#avg-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.avg_pool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.avg_pool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.avg_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies 3D average-pooling operation in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>D</mi><mtext> </mtext><mi>t</mi><mi>i</mi><mi>m</mi><mi>e</mi><mi>s</mi><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kD \ times kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mspace"> </span><span class="mord mathnormal">t</span><span class="mord mathnormal">im</span><span class="mord mathnormal">es</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by step size
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>s</mi><mi>D</mi><mo>×</mo><mi>s</mi><mi>H</mi><mo>×</mo><mi>s</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">sD \times sH \times sW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">sD</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">sH</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">s</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span></span></span></span></span> steps. The number of output features is equal to the number of
 input planes.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.celu.html b/main/generated/torch.ao.nn.quantized.functional.celu.html
index 1da2803f313..38bd5001cd2 100644
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>celu<a class="headerlink" href="#celu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.celu">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">celu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#celu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.celu" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">celu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#celu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.celu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the quantized CELU function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>CELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mi mathvariant="normal">/</mi><mi>α</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x / \alpha) - 1))
diff --git a/main/generated/torch.ao.nn.quantized.functional.clamp.html b/main/generated/torch.ao.nn.quantized.functional.clamp.html
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@@ -614,7 +614,7 @@
 <h1>clamp<a class="headerlink" href="#clamp" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.clamp">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">clamp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#clamp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L659"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.clamp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">clamp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#clamp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L659"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.clamp" title="Permalink to this definition">¶</a></dt>
 <dd><p>float(input, min_, max_) -&gt; Tensor</p>
 <p>Applies the clamp function element-wise.
 See <code class="xref py py-class docutils literal notranslate"><span class="pre">clamp</span></code> for more details.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.conv1d.html b/main/generated/torch.ao.nn.quantized.functional.conv1d.html
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           <div class="pytorch-left-menu-search">
             
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>conv1d<a class="headerlink" href="#conv1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.conv1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D convolution over a quantized 1D input composed of several input
 planes.</p>
 <p>See <a class="reference internal" href="torch.ao.nn.quantized.Conv1d.html#torch.ao.nn.quantized.Conv1d" title="torch.ao.nn.quantized.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a> for details and output shape.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.conv2d.html b/main/generated/torch.ao.nn.quantized.functional.conv2d.html
index e012af2b29e..385a06d4e51 100644
--- a/main/generated/torch.ao.nn.quantized.functional.conv2d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.conv2d.html
@@ -300,7 +300,7 @@
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     </div>
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@@ -614,7 +614,7 @@
 <h1>conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.conv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D convolution over a quantized 2D input composed of several input
 planes.</p>
 <p>See <a class="reference internal" href="torch.ao.nn.quantized.Conv2d.html#torch.ao.nn.quantized.Conv2d" title="torch.ao.nn.quantized.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a> for details and output shape.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.conv3d.html b/main/generated/torch.ao.nn.quantized.functional.conv3d.html
index 77fbc1fcd59..bff5b7fd3aa 100644
--- a/main/generated/torch.ao.nn.quantized.functional.conv3d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.conv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.conv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D convolution over a quantized 3D input composed of several input
 planes.</p>
 <p>See <a class="reference internal" href="torch.ao.nn.quantized.Conv3d.html#torch.ao.nn.quantized.Conv3d" title="torch.ao.nn.quantized.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code></a> for details and output shape.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.elu.html b/main/generated/torch.ao.nn.quantized.functional.elu.html
index 890ef2621a7..6582f1c3f04 100644
--- a/main/generated/torch.ao.nn.quantized.functional.elu.html
+++ b/main/generated/torch.ao.nn.quantized.functional.elu.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>elu<a class="headerlink" href="#elu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.elu">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">elu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#elu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.elu" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">elu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#elu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.elu" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.functional.elu.html#torch.nn.functional.elu" title="torch.nn.functional.elu"><code class="xref py py-func docutils literal notranslate"><span class="pre">elu()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.functional.hardsigmoid.html b/main/generated/torch.ao.nn.quantized.functional.hardsigmoid.html
index 412c8cbcb11..377c002c99c 100644
--- a/main/generated/torch.ao.nn.quantized.functional.hardsigmoid.html
+++ b/main/generated/torch.ao.nn.quantized.functional.hardsigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>hardsigmoid<a class="headerlink" href="#hardsigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.hardsigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardsigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardsigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.functional.hardsigmoid.html#torch.nn.functional.hardsigmoid" title="torch.nn.functional.hardsigmoid"><code class="xref py py-func docutils literal notranslate"><span class="pre">hardsigmoid()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.ao.nn.quantized.functional.hardswish.html b/main/generated/torch.ao.nn.quantized.functional.hardswish.html
index e8694225e9e..c9f202afeb4 100644
--- a/main/generated/torch.ao.nn.quantized.functional.hardswish.html
+++ b/main/generated/torch.ao.nn.quantized.functional.hardswish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>hardswish<a class="headerlink" href="#hardswish" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.hardswish">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L603"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardswish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L603"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardswish" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.functional.hardswish.html#torch.nn.functional.hardswish" title="torch.nn.functional.hardswish"><code class="xref py py-func docutils literal notranslate"><span class="pre">hardswish()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.functional.hardtanh.html b/main/generated/torch.ao.nn.quantized.functional.hardtanh.html
index 2b85fc79c26..e303e1a414e 100644
--- a/main/generated/torch.ao.nn.quantized.functional.hardtanh.html
+++ b/main/generated/torch.ao.nn.quantized.functional.hardtanh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>hardtanh<a class="headerlink" href="#hardtanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.hardtanh">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardtanh" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardtanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.functional.hardtanh.html#torch.nn.functional.hardtanh" title="torch.nn.functional.hardtanh"><code class="xref py py-func docutils literal notranslate"><span class="pre">hardtanh()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.ao.nn.quantized.functional.interpolate.html b/main/generated/torch.ao.nn.quantized.functional.interpolate.html
index edd5fc78b22..a2b8a781361 100644
--- a/main/generated/torch.ao.nn.quantized.functional.interpolate.html
+++ b/main/generated/torch.ao.nn.quantized.functional.interpolate.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>interpolate<a class="headerlink" href="#interpolate" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.interpolate">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#interpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.interpolate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#interpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.interpolate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Down/up samples the input to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
 <p>See <a class="reference internal" href="torch.nn.functional.interpolate.html#torch.nn.functional.interpolate" title="torch.nn.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.interpolate()</span></code></a> for implementation details.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.leaky_relu.html b/main/generated/torch.ao.nn.quantized.functional.leaky_relu.html
index e53323a99c8..ee7e26f3724 100644
--- a/main/generated/torch.ao.nn.quantized.functional.leaky_relu.html
+++ b/main/generated/torch.ao.nn.quantized.functional.leaky_relu.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>leaky_relu<a class="headerlink" href="#leaky-relu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.leaky_relu">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">leaky_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#leaky_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L556"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.leaky_relu" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">leaky_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#leaky_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L556"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.leaky_relu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Quantized version of the.
 leaky_relu(input, negative_slope=0.01, inplace=False, scale, zero_point) -&gt; Tensor</p>
 <p>Applies element-wise,
diff --git a/main/generated/torch.ao.nn.quantized.functional.linear.html b/main/generated/torch.ao.nn.quantized.functional.linear.html
index 6796b5e3e8c..fe87d75ffd0 100644
--- a/main/generated/torch.ao.nn.quantized.functional.linear.html
+++ b/main/generated/torch.ao.nn.quantized.functional.linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L438"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L438"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a linear transformation to the incoming quantized data:
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>x</mi><msup><mi>A</mi><mi>T</mi></msup><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">y = xA^T + b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.9247em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">x</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span>.
 See <a class="reference internal" href="torch.ao.nn.quantized.Linear.html#torch.ao.nn.quantized.Linear" title="torch.ao.nn.quantized.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a></p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.max_pool1d.html b/main/generated/torch.ao.nn.quantized.functional.max_pool1d.html
index 5e8b89dad98..a978ba50248 100644
--- a/main/generated/torch.ao.nn.quantized.functional.max_pool1d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.max_pool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>max_pool1d<a class="headerlink" href="#max-pool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.max_pool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#max_pool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.max_pool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#max_pool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.max_pool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D max pooling over a quantized input signal composed of
 several quantized input planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.ao.nn.quantized.functional.max_pool2d.html b/main/generated/torch.ao.nn.quantized.functional.max_pool2d.html
index f9635d4f253..29dea11f3b4 100644
--- a/main/generated/torch.ao.nn.quantized.functional.max_pool2d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.max_pool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>max_pool2d<a class="headerlink" href="#max-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.max_pool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#max_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#max_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D max pooling over a quantized input signal composed of
 several quantized input planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.ao.nn.quantized.functional.threshold.html b/main/generated/torch.ao.nn.quantized.functional.threshold.html
index 3bd15a064be..939322a945a 100644
--- a/main/generated/torch.ao.nn.quantized.functional.threshold.html
+++ b/main/generated/torch.ao.nn.quantized.functional.threshold.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>threshold<a class="headerlink" href="#threshold" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.threshold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.threshold" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.threshold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the quantized version of the threshold function element-wise:</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>x</mi><mo>=</mo><mrow><mo fence="true">{</mo><mtable rowspacing="0.36em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mi>x</mi></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext>if </mtext><mi>x</mi><mo>&gt;</mo><mtext>threshold</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mtext>value</mtext></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mtext>otherwise</mtext></mstyle></mtd></mtr></mtable></mrow></mrow><annotation encoding="application/x-tex">x = \begin{cases}
diff --git a/main/generated/torch.ao.nn.quantized.functional.upsample.html b/main/generated/torch.ao.nn.quantized.functional.upsample.html
index 5997b8beee6..64e00e8a78a 100644
--- a/main/generated/torch.ao.nn.quantized.functional.upsample.html
+++ b/main/generated/torch.ao.nn.quantized.functional.upsample.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>upsample<a class="headerlink" href="#upsample" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.upsample">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
 <div class="admonition warning">
diff --git a/main/generated/torch.ao.nn.quantized.functional.upsample_bilinear.html b/main/generated/torch.ao.nn.quantized.functional.upsample_bilinear.html
index e364254f37e..c45ca9742e6 100644
--- a/main/generated/torch.ao.nn.quantized.functional.upsample_bilinear.html
+++ b/main/generated/torch.ao.nn.quantized.functional.upsample_bilinear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>upsample_bilinear<a class="headerlink" href="#upsample-bilinear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.upsample_bilinear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample_bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L731"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample_bilinear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample_bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L731"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample_bilinear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input, using bilinear upsampling.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.upsample_nearest.html b/main/generated/torch.ao.nn.quantized.functional.upsample_nearest.html
index c63d0ff24bf..a6c2d980f94 100644
--- a/main/generated/torch.ao.nn.quantized.functional.upsample_nearest.html
+++ b/main/generated/torch.ao.nn.quantized.functional.upsample_nearest.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>upsample_nearest<a class="headerlink" href="#upsample-nearest" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.upsample_nearest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/nn/quantized/functional.py#L756"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample_nearest" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L756"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample_nearest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input, using nearest neighbours’ pixel values.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.ao.quantization.CUSTOM_KEY.html b/main/generated/torch.ao.quantization.CUSTOM_KEY.html
index 821ebcaddb1..f5c6a9251c3 100644
--- a/main/generated/torch.ao.quantization.CUSTOM_KEY.html
+++ b/main/generated/torch.ao.quantization.CUSTOM_KEY.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.DeQuantStub.html b/main/generated/torch.ao.quantization.DeQuantStub.html
index 3d9c7ceaac0..ab4d994f46a 100644
--- a/main/generated/torch.ao.quantization.DeQuantStub.html
+++ b/main/generated/torch.ao.quantization.DeQuantStub.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>DeQuantStub<a class="headerlink" href="#dequantstub" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.DeQuantStub">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">DeQuantStub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#DeQuantStub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/stubs.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.DeQuantStub" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">DeQuantStub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#DeQuantStub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/stubs.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.DeQuantStub" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dequantize stub module, before calibration, this is same as identity,
 this will be swapped as <cite>nnq.DeQuantize</cite> in <cite>convert</cite>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.NUMERIC_DEBUG_HANDLE_KEY.html b/main/generated/torch.ao.quantization.NUMERIC_DEBUG_HANDLE_KEY.html
index 11590ec538e..98d6e0d3362 100644
--- a/main/generated/torch.ao.quantization.NUMERIC_DEBUG_HANDLE_KEY.html
+++ b/main/generated/torch.ao.quantization.NUMERIC_DEBUG_HANDLE_KEY.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.QuantStub.html b/main/generated/torch.ao.quantization.QuantStub.html
index a66aa76bca4..7de202bde04 100644
--- a/main/generated/torch.ao.quantization.QuantStub.html
+++ b/main/generated/torch.ao.quantization.QuantStub.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>QuantStub<a class="headerlink" href="#quantstub" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.QuantStub">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">QuantStub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#QuantStub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/stubs.py#L6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.QuantStub" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">QuantStub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#QuantStub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/stubs.py#L6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.QuantStub" title="Permalink to this definition">¶</a></dt>
 <dd><p>Quantize stub module, before calibration, this is same as an observer,
 it will be swapped as <cite>nnq.Quantize</cite> in <cite>convert</cite>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.QuantWrapper.html b/main/generated/torch.ao.quantization.QuantWrapper.html
index 3950924268c..48dd5681aea 100644
--- a/main/generated/torch.ao.quantization.QuantWrapper.html
+++ b/main/generated/torch.ao.quantization.QuantWrapper.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>QuantWrapper<a class="headerlink" href="#quantwrapper" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.QuantWrapper">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">QuantWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#QuantWrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/stubs.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.QuantWrapper" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">QuantWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#QuantWrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/stubs.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.QuantWrapper" title="Permalink to this definition">¶</a></dt>
 <dd><p>A wrapper class that wraps the input module, adds QuantStub and
 DeQuantStub and surround the call to module with call to quant and dequant
 modules.</p>
diff --git a/main/generated/torch.ao.quantization.add_quant_dequant.html b/main/generated/torch.ao.quantization.add_quant_dequant.html
index 056ab85cbfc..0b84e94e06f 100644
--- a/main/generated/torch.ao.quantization.add_quant_dequant.html
+++ b/main/generated/torch.ao.quantization.add_quant_dequant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>add_quant_dequant<a class="headerlink" href="#add-quant-dequant" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.add_quant_dequant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">add_quant_dequant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#add_quant_dequant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.add_quant_dequant" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">add_quant_dequant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#add_quant_dequant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.add_quant_dequant" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap the leaf child module in QuantWrapper if it has a valid qconfig
 Note that this function will modify the children of module inplace and it
 can return a new module which wraps the input module as well.</p>
diff --git a/main/generated/torch.ao.quantization.backend_config.BackendConfig.html b/main/generated/torch.ao.quantization.backend_config.BackendConfig.html
index 90ebfa3b79b..4bbf9e9ddcf 100644
--- a/main/generated/torch.ao.quantization.backend_config.BackendConfig.html
+++ b/main/generated/torch.ao.quantization.backend_config.BackendConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">BackendConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">BackendConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Config that defines the set of patterns that can be quantized on a given backend, and how reference
 quantized models can be produced from these patterns.</p>
 <p>A pattern in this context refers to a module, a functional, an operator, or a directed acyclic graph
@@ -667,7 +667,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">BackendConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“name”: the name of the target backend</p>
@@ -682,7 +682,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_config">
-<span class="sig-name descname"><span class="pre">set_backend_pattern_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_backend_pattern_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_config" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_backend_pattern_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_backend_pattern_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_config" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the config for an pattern that can be run on the target backend.
 This overrides any existing config for the given pattern.</p>
 <dl class="field-list simple">
@@ -694,7 +694,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_configs">
-<span class="sig-name descname"><span class="pre">set_backend_pattern_configs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">configs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_backend_pattern_configs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_configs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_backend_pattern_configs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">configs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_backend_pattern_configs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_configs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the configs for patterns that can be run on the target backend.
 This overrides any existing config for a given pattern if it was previously registered already.</p>
 <dl class="field-list simple">
@@ -706,7 +706,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.set_name">
-<span class="sig-name descname"><span class="pre">set_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the name of the target backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -717,7 +717,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">BackendConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.backend_config.BackendConfig.from_dict" title="torch.ao.quantization.backend_config.BackendConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.backend_config.BackendPatternConfig.html b/main/generated/torch.ao.quantization.backend_config.BackendPatternConfig.html
index 58dfbdb57ac..9185f56f71a 100644
--- a/main/generated/torch.ao.quantization.backend_config.BackendPatternConfig.html
+++ b/main/generated/torch.ao.quantization.backend_config.BackendPatternConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,14 +614,14 @@
 <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">BackendPatternConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">BackendPatternConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Config object that specifies quantization behavior for a given operator pattern.
 For a detailed example usage, see <a class="reference internal" href="torch.ao.quantization.backend_config.BackendConfig.html#torch.ao.quantization.backend_config.BackendConfig" title="torch.ao.quantization.backend_config.BackendConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">BackendConfig</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.add_dtype_config">
-<span class="sig-name descname"><span class="pre">add_dtype_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.add_dtype_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.add_dtype_config" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_dtype_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.add_dtype_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.add_dtype_config" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a set of supported data types passed as arguments to quantize ops in the
 reference model spec.</p>
 <dl class="field-list simple">
@@ -633,7 +633,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_pattern_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L630"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_pattern_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L630"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">BackendPatternConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“pattern”: the pattern being configured
@@ -657,7 +657,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_dtype_configs">
-<span class="sig-name descname"><span class="pre">set_dtype_configs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_configs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_dtype_configs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_dtype_configs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_dtype_configs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_configs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_dtype_configs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_dtype_configs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the supported data types passed as arguments to quantize ops in the
 reference model spec, overriding all previously registered data types.</p>
 <dl class="field-list simple">
@@ -669,7 +669,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_fused_module">
-<span class="sig-name descname"><span class="pre">set_fused_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fused_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_fused_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_fused_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_fused_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fused_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_fused_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_fused_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module that represents the fused implementation for this pattern.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -680,7 +680,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_fuser_method">
-<span class="sig-name descname"><span class="pre">set_fuser_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fuser_method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_fuser_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L576"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_fuser_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_fuser_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fuser_method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_fuser_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L576"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_fuser_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the function that specifies how to fuse this BackendPatternConfig’s pattern.</p>
 <p>The first argument of this function should be <cite>is_qat</cite>, and the rest of the arguments
 should be the items in the tuple pattern. The return value of this function should be
@@ -702,7 +702,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_observation_type">
-<span class="sig-name descname"><span class="pre">set_observation_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observation_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_observation_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_observation_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_observation_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observation_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_observation_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_observation_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set how observers should be inserted in the graph for this pattern.</p>
 <p>Observation type here refers to how observers (or quant-dequant ops) will be placed
 in the graph. This is used to produce the desired reference patterns understood by
@@ -727,7 +727,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_pattern">
-<span class="sig-name descname"><span class="pre">set_pattern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_pattern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_pattern" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_pattern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_pattern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_pattern" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the pattern to configure.</p>
 <p>The pattern can be a float module, functional operator, pytorch operator, or a tuple
 combination of the above. Tuple patterns are treated as sequential patterns, and
@@ -741,7 +741,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_qat_module">
-<span class="sig-name descname"><span class="pre">set_qat_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qat_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_qat_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_qat_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_qat_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qat_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_qat_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_qat_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module that represents the QAT implementation for this pattern.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -752,7 +752,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_reference_quantized_module">
-<span class="sig-name descname"><span class="pre">set_reference_quantized_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_quantized_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_reference_quantized_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_reference_quantized_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_reference_quantized_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_quantized_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_reference_quantized_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_reference_quantized_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module that represents the reference quantized implementation for
 this pattern’s root module.</p>
 <p>For more detail, see <a class="reference internal" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module" title="torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_root_module()</span></code></a>.</p>
@@ -765,7 +765,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module">
-<span class="sig-name descname"><span class="pre">set_root_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_root_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_root_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_root_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module that represents the root for this pattern.</p>
 <p>When we construct the reference quantized model during the convert phase,
 the root modules (e.g. torch.nn.Linear for torch.ao.nn.intrinsic.LinearReLU)
@@ -784,7 +784,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L706"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L706"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">BackendPatternConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.backend_config.BackendPatternConfig.from_dict" title="torch.ao.quantization.backend_config.BackendPatternConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.backend_config.DTypeConfig.html b/main/generated/torch.ao.quantization.backend_config.DTypeConfig.html
index 3cbf2e7fbba..eae9d53797a 100644
--- a/main/generated/torch.ao.quantization.backend_config.DTypeConfig.html
+++ b/main/generated/torch.ao.quantization.backend_config.DTypeConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>DTypeConfig<a class="headerlink" href="#dtypeconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.DTypeConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">DTypeConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">DTypeConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Config object that specifies the supported data types passed as arguments to
 quantize ops in the reference model spec, for input and output activations,
 weights, and biases.</p>
@@ -680,7 +680,7 @@ <h1>DTypeConfig<a class="headerlink" href="#dtypeconfig" title="Permalink to thi
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.DTypeConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Create a <code class="docutils literal notranslate"><span class="pre">DTypeConfig</span></code> from a dictionary with the following items (all optional):</dt><dd><p>“input_dtype”: torch.dtype or <code class="docutils literal notranslate"><span class="pre">DTypeWithConstraints</span></code>
 “output_dtype”: torch.dtype or <code class="docutils literal notranslate"><span class="pre">DTypeWithConstraints</span></code>
@@ -698,7 +698,7 @@ <h1>DTypeConfig<a class="headerlink" href="#dtypeconfig" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.DTypeConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">DTypeConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.backend_config.DTypeConfig.from_dict" title="torch.ao.quantization.backend_config.DTypeConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.backend_config.DTypeWithConstraints.html b/main/generated/torch.ao.quantization.backend_config.DTypeWithConstraints.html
index e19f3dd2bda..629da8d50df 100644
--- a/main/generated/torch.ao.quantization.backend_config.DTypeWithConstraints.html
+++ b/main/generated/torch.ao.quantization.backend_config.DTypeWithConstraints.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>DTypeWithConstraints<a class="headerlink" href="#dtypewithconstraints" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.DTypeWithConstraints">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">DTypeWithConstraints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max_upper_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_min_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_max_upper_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_exact_match</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_exact_match</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeWithConstraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeWithConstraints" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">DTypeWithConstraints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max_upper_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_min_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_max_upper_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_exact_match</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_exact_match</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeWithConstraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeWithConstraints" title="Permalink to this definition">¶</a></dt>
 <dd><p>Config for specifying additional constraints for a given dtype, such as quantization
 value ranges, scale value ranges, and fixed quantization params, to be used in
 <a class="reference internal" href="torch.ao.quantization.backend_config.DTypeConfig.html#torch.ao.quantization.backend_config.DTypeConfig" title="torch.ao.quantization.backend_config.DTypeConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTypeConfig</span></code></a>.</p>
diff --git a/main/generated/torch.ao.quantization.backend_config.ObservationType.html b/main/generated/torch.ao.quantization.backend_config.ObservationType.html
index 68efd0bbb35..4145c3f4da3 100644
--- a/main/generated/torch.ao.quantization.backend_config.ObservationType.html
+++ b/main/generated/torch.ao.quantization.backend_config.ObservationType.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ObservationType<a class="headerlink" href="#observationtype" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.ObservationType">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">ObservationType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#ObservationType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/backend_config/backend_config.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.ObservationType" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">ObservationType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#ObservationType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.ObservationType" title="Permalink to this definition">¶</a></dt>
 <dd><p>An enum that represents different ways of how an operator/operator pattern
 should be observed</p>
 <dl class="py attribute">
diff --git a/main/generated/torch.ao.quantization.compare_results.html b/main/generated/torch.ao.quantization.compare_results.html
index 52d2cf993d9..d8504726e0c 100644
--- a/main/generated/torch.ao.quantization.compare_results.html
+++ b/main/generated/torch.ao.quantization.compare_results.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>compare_results<a class="headerlink" href="#compare-results" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.compare_results">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">compare_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">actual_results</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/pt2e/_numeric_debugger.py#L297"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.compare_results" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">compare_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">actual_results</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/_numeric_debugger.py#L297"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.compare_results" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given two dict mapping from <cite>debug_handle_id</cite> (int) to list of tensors
 return a map from <cite>debug_handle_id</cite> to <cite>NodeAccuracySummary</cite> that contains
 comparison information like SQNR, MSE etc.</p>
diff --git a/main/generated/torch.ao.quantization.convert.html b/main/generated/torch.ao.quantization.convert.html
index 8655737d06f..1b1d7e0067b 100644
--- a/main/generated/torch.ao.quantization.convert.html
+++ b/main/generated/torch.ao.quantization.convert.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>convert<a class="headerlink" href="#convert" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.convert">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">convert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_reference</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convert_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#convert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.convert" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">convert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_reference</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convert_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#convert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.convert" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts submodules in input module to a different module according to <cite>mapping</cite>
 by calling <cite>from_float</cite> method on the target module class. And remove qconfig at the
 end if remove_qconfig is set to True.</p>
diff --git a/main/generated/torch.ao.quantization.default_eval_fn.html b/main/generated/torch.ao.quantization.default_eval_fn.html
index 72a4a5a1c6b..0f7b7f6b53c 100644
--- a/main/generated/torch.ao.quantization.default_eval_fn.html
+++ b/main/generated/torch.ao.quantization.default_eval_fn.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>default_eval_fn<a class="headerlink" href="#default-eval-fn" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.default_eval_fn">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">default_eval_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calib_data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization.html#default_eval_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/__init__.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.default_eval_fn" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">default_eval_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calib_data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization.html#default_eval_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/__init__.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.default_eval_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the default evaluation function.</p>
 <p>Default evaluation function takes a torch.utils.data.Dataset or a list of
 input Tensors and run the model on the dataset</p>
diff --git a/main/generated/torch.ao.quantization.extract_results_from_loggers.html b/main/generated/torch.ao.quantization.extract_results_from_loggers.html
index 40161879501..f8bd72011e9 100644
--- a/main/generated/torch.ao.quantization.extract_results_from_loggers.html
+++ b/main/generated/torch.ao.quantization.extract_results_from_loggers.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>extract_results_from_loggers<a class="headerlink" href="#extract-results-from-loggers" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.extract_results_from_loggers">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">extract_results_from_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/pt2e/_numeric_debugger.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.extract_results_from_loggers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">extract_results_from_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/_numeric_debugger.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.extract_results_from_loggers" title="Permalink to this definition">¶</a></dt>
 <dd><p>For a given model, extract the tensors stats and related information for each debug handle.
 The reason we have a list of object, instead of Tensor is because the output of node may not be
 a Tensor, it could be (nested) list, tuple or dict as well.</p>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.FakeQuantize.html b/main/generated/torch.ao.quantization.fake_quantize.FakeQuantize.html
index e75fdf61fae..dd790c05298 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.FakeQuantize.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.FakeQuantize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>FakeQuantize<a class="headerlink" href="#fakequantize" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FakeQuantize">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer=&lt;class</span> <span class="pre">'torch.ao.quantization.observer.MovingAverageMinMaxObserver'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**observer_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fake_quantize.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FakeQuantize" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer=&lt;class</span> <span class="pre">'torch.ao.quantization.observer.MovingAverageMinMaxObserver'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**observer_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FakeQuantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simulate the quantize and dequantize operations in training time.</p>
 <p>The output of this module is given by:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x_out</span> <span class="o">=</span> <span class="p">(</span>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.FakeQuantizeBase.html b/main/generated/torch.ao.quantization.fake_quantize.FakeQuantizeBase.html
index 64c4aec6fe0..f3a64a931dd 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.FakeQuantizeBase.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -614,7 +614,7 @@
 <h1>FakeQuantizeBase<a class="headerlink" href="#fakequantizebase" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FakeQuantizeBase">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FakeQuantizeBase</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FakeQuantizeBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fake_quantize.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FakeQuantizeBase" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FakeQuantizeBase</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FakeQuantizeBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FakeQuantizeBase" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base fake quantize module.</p>
 <p>Base fake quantize module
 Any fake quantize implementation should derive from this class.</p>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html b/main/generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html
index e22286c7c68..b7aff96c8c9 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -614,14 +614,14 @@
 <h1>FixedQParamsFakeQuantize<a class="headerlink" href="#fixedqparamsfakequantize" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FixedQParamsFakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FixedQParamsFakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fake_quantize.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FixedQParamsFakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FixedQParamsFakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simulate quantize and dequantize in training time.</p>
 <p>Simulate quantize and dequantize with fixed quantization
 parameters in training time. Only per tensor quantization
 is supported.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FixedQParamsFakeQuantize.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fake_quantize.py#L347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FixedQParamsFakeQuantize.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a string representation of the object’s attributes.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html b/main/generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html
index 9e646db8e5c..f24c40e7299 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>FusedMovingAvgObsFakeQuantize<a class="headerlink" href="#fusedmovingavgobsfakequantize" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FusedMovingAvgObsFakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer=&lt;class</span> <span class="pre">'torch.ao.quantization.observer.MovingAverageMinMaxObserver'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max=255</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**observer_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FusedMovingAvgObsFakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fake_quantize.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FusedMovingAvgObsFakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer=&lt;class</span> <span class="pre">'torch.ao.quantization.observer.MovingAverageMinMaxObserver'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max=255</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**observer_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FusedMovingAvgObsFakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a fused module to observe the tensor.</p>
 <p>Fused module that is used to observe the input tensor (compute min/max), compute
 scale/zero_point and fake_quantize the tensor.
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_fake_quant.html
index 9c7d7120878..4ab62299a5b 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_fused_act_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_fused_act_fake_quant.html
index b44651408b0..1e3ee3f74cc 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_fused_act_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_fused_act_fake_quant.html
@@ -300,7 +300,7 @@
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html
index 4a255c905a3..19e0fd91fd5 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html
index 88f7c3f8940..616aa9db085 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html
index 95a5f8a888c..29537e93649 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
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diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html
index 35e5d449ce4..758dc0fa174 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html
index ac796b60a2d..058abb52bfe 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html
index 593720fd691..0fcb90ab657 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>disable_fake_quant<a class="headerlink" href="#disable-fake-quant" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.disable_fake_quant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">disable_fake_quant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#disable_fake_quant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fake_quantize.py#L598"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.disable_fake_quant" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">disable_fake_quant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#disable_fake_quant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L598"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.disable_fake_quant" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable fake quantization for the module.</p>
 <p>Disable fake quantization for this module, if applicable. Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is any PyTorch model</span>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.disable_observer.html b/main/generated/torch.ao.quantization.fake_quantize.disable_observer.html
index fc8f15d41f1..9b74966d799 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.disable_observer.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.disable_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>disable_observer<a class="headerlink" href="#disable-observer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.disable_observer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">disable_observer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#disable_observer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fake_quantize.py#L624"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.disable_observer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">disable_observer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#disable_observer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L624"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.disable_observer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable observation for this module.</p>
 <p>Disable observation for this module, if applicable. Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is any PyTorch model</span>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html
index db51ede9a0c..baa3982120c 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>enable_fake_quant<a class="headerlink" href="#enable-fake-quant" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.enable_fake_quant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">enable_fake_quant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#enable_fake_quant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fake_quantize.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.enable_fake_quant" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">enable_fake_quant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#enable_fake_quant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.enable_fake_quant" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable fake quantization for the module.</p>
 <p>Enable fake quantization for this module, if applicable. Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is any PyTorch model</span>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.enable_observer.html b/main/generated/torch.ao.quantization.fake_quantize.enable_observer.html
index 2f66a299c3d..175480a3bb5 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.enable_observer.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.enable_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>enable_observer<a class="headerlink" href="#enable-observer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.enable_observer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">enable_observer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#enable_observer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fake_quantize.py#L637"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.enable_observer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">enable_observer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#enable_observer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L637"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.enable_observer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable observation for this module.</p>
 <p>Enable observation for this module, if applicable. Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is any PyTorch model</span>
diff --git a/main/generated/torch.ao.quantization.fuse_modules.fuse_modules.html b/main/generated/torch.ao.quantization.fuse_modules.fuse_modules.html
index 5d874e8b01a..8da2f860b9e 100644
--- a/main/generated/torch.ao.quantization.fuse_modules.fuse_modules.html
+++ b/main/generated/torch.ao.quantization.fuse_modules.fuse_modules.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>fuse_modules<a class="headerlink" href="#fuse-modules" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fuse_modules.fuse_modules">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fuse_modules.</span></span><span class="sig-name descname"><span class="pre">fuse_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modules_to_fuse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuser_func=&lt;function</span> <span class="pre">fuse_known_modules&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuse_custom_config_dict=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fuse_modules.html#fuse_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fuse_modules.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fuse_modules.fuse_modules" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fuse_modules.</span></span><span class="sig-name descname"><span class="pre">fuse_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modules_to_fuse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuser_func=&lt;function</span> <span class="pre">fuse_known_modules&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuse_custom_config_dict=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fuse_modules.html#fuse_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fuse_modules.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fuse_modules.fuse_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse a list of modules into a single module.</p>
 <p>Fuses only the following sequence of modules:
 conv, bn
diff --git a/main/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html b/main/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html
index f8aecead0b3..bd94ffbb929 100644
--- a/main/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html
+++ b/main/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">ConvertCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">ConvertCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Custom configuration for <a class="reference internal" href="torch.ao.quantization.quantize_fx.convert_fx.html#torch.ao.quantization.quantize_fx.convert_fx" title="torch.ao.quantization.quantize_fx.convert_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">convert_fx()</span></code></a>.</p>
 <p>Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">convert_custom_config</span> <span class="o">=</span> <span class="n">ConvertCustomConfig</span><span class="p">()</span>             <span class="o">.</span><span class="n">set_observed_to_quantized_mapping</span><span class="p">(</span><span class="n">ObservedCustomModule</span><span class="p">,</span> <span class="n">QuantizedCustomModule</span><span class="p">)</span>             <span class="o">.</span><span class="n">set_preserved_attributes</span><span class="p">([</span><span class="s2">&quot;attr1&quot;</span><span class="p">,</span> <span class="s2">&quot;attr2&quot;</span><span class="p">])</span>
@@ -624,7 +624,7 @@ <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">convert_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">convert_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">ConvertCustomConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“observed_to_quantized_custom_module_class”: a nested dictionary mapping from quantization
@@ -646,7 +646,7 @@ <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_observed_to_quantized_mapping">
-<span class="sig-name descname"><span class="pre">set_observed_to_quantized_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observed_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantized_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">QuantType.STATIC</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.set_observed_to_quantized_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_observed_to_quantized_mapping" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_observed_to_quantized_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observed_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantized_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">QuantType.STATIC</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.set_observed_to_quantized_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_observed_to_quantized_mapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the mapping from a custom observed module class to a custom quantized module class.</p>
 <p>The quantized module class must have a <code class="docutils literal notranslate"><span class="pre">from_observed</span></code> class method that converts the observed module class
 to the quantized module class.</p>
@@ -659,7 +659,7 @@ <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_preserved_attributes">
-<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the names of the attributes that will persist in the graph module even if they are not used in
 the model’s <code class="docutils literal notranslate"><span class="pre">forward</span></code> method.</p>
 <dl class="field-list simple">
@@ -671,7 +671,7 @@ <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">ConvertCustomConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict" title="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html b/main/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html
index c08cc514e1e..41f0dd7920f 100644
--- a/main/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html
+++ b/main/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>FuseCustomConfig<a class="headerlink" href="#fusecustomconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.FuseCustomConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">FuseCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">FuseCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Custom configuration for <a class="reference internal" href="torch.ao.quantization.quantize_fx.fuse_fx.html#torch.ao.quantization.quantize_fx.fuse_fx" title="torch.ao.quantization.quantize_fx.fuse_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">fuse_fx()</span></code></a>.</p>
 <p>Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fuse_custom_config</span> <span class="o">=</span> <span class="n">FuseCustomConfig</span><span class="p">()</span><span class="o">.</span><span class="n">set_preserved_attributes</span><span class="p">([</span><span class="s2">&quot;attr1&quot;</span><span class="p">,</span> <span class="s2">&quot;attr2&quot;</span><span class="p">])</span>
@@ -624,7 +624,7 @@ <h1>FuseCustomConfig<a class="headerlink" href="#fusecustomconfig" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.FuseCustomConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fuse_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fuse_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">ConvertCustomConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“preserved_attributes”: a list of attributes that persist even if they are not used in <code class="docutils literal notranslate"><span class="pre">forward</span></code></p>
@@ -639,7 +639,7 @@ <h1>FuseCustomConfig<a class="headerlink" href="#fusecustomconfig" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.FuseCustomConfig.set_preserved_attributes">
-<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the names of the attributes that will persist in the graph module even if they are not used in
 the model’s <code class="docutils literal notranslate"><span class="pre">forward</span></code> method.</p>
 <dl class="field-list simple">
@@ -651,7 +651,7 @@ <h1>FuseCustomConfig<a class="headerlink" href="#fusecustomconfig" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.FuseCustomConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">FuseCustomConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict" title="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html b/main/generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html
index b9dea75537f..d333abab0aa 100644
--- a/main/generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html
+++ b/main/generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">PrepareCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">PrepareCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Custom configuration for <a class="reference internal" href="torch.ao.quantization.quantize_fx.prepare_fx.html#torch.ao.quantization.quantize_fx.prepare_fx" title="torch.ao.quantization.quantize_fx.prepare_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">prepare_fx()</span></code></a> and
 <a class="reference internal" href="torch.ao.quantization.quantize_fx.prepare_qat_fx.html#torch.ao.quantization.quantize_fx.prepare_qat_fx" title="torch.ao.quantization.quantize_fx.prepare_qat_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">prepare_qat_fx()</span></code></a>.</p>
 <p>Example usage:</p>
@@ -625,7 +625,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">PrepareCustomConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“standalone_module_name”: a list of (module_name, qconfig_mapping, example_inputs,
@@ -651,7 +651,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_float_to_observed_mapping">
-<span class="sig-name descname"><span class="pre">set_float_to_observed_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observed_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">QuantType.STATIC</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_float_to_observed_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_float_to_observed_mapping" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_float_to_observed_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observed_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">QuantType.STATIC</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_float_to_observed_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_float_to_observed_mapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the mapping from a custom float module class to a custom observed module class.</p>
 <p>The observed module class must have a <code class="docutils literal notranslate"><span class="pre">from_float</span></code> class method that converts the float module class
 to the observed module class. This is currently only supported for static quantization.</p>
@@ -664,7 +664,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_input_quantized_indexes">
-<span class="sig-name descname"><span class="pre">set_input_quantized_indexes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_input_quantized_indexes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_input_quantized_indexes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_input_quantized_indexes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_input_quantized_indexes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_input_quantized_indexes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the indexes of the inputs of the graph that should be quantized.
 Inputs are otherwise assumed to be in fp32 by default instead.</p>
 <dl class="field-list simple">
@@ -676,7 +676,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_classes">
-<span class="sig-name descname"><span class="pre">set_non_traceable_module_classes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_non_traceable_module_classes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_classes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_non_traceable_module_classes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_non_traceable_module_classes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_classes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the modules that are not symbolically traceable, identified by class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -687,7 +687,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_names">
-<span class="sig-name descname"><span class="pre">set_non_traceable_module_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_non_traceable_module_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_non_traceable_module_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_non_traceable_module_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_names" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the modules that are not symbolically traceable, identified by name.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -698,7 +698,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_output_quantized_indexes">
-<span class="sig-name descname"><span class="pre">set_output_quantized_indexes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_output_quantized_indexes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_output_quantized_indexes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_output_quantized_indexes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_output_quantized_indexes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_output_quantized_indexes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the indexes of the outputs of the graph that should be quantized.
 Outputs are otherwise assumed to be in fp32 by default instead.</p>
 <dl class="field-list simple">
@@ -710,7 +710,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_preserved_attributes">
-<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the names of the attributes that will persist in the graph module even if they are not used in
 the model’s <code class="docutils literal notranslate"><span class="pre">forward</span></code> method.</p>
 <dl class="field-list simple">
@@ -722,7 +722,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_class">
-<span class="sig-name descname"><span class="pre">set_standalone_module_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_standalone_module_class"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_class" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_standalone_module_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_standalone_module_class"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_class" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the configuration for running a standalone module identified by <code class="docutils literal notranslate"><span class="pre">module_class</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">qconfig_mapping</span></code> is None, the parent <code class="docutils literal notranslate"><span class="pre">qconfig_mapping</span></code> will be used instead.
 If <code class="docutils literal notranslate"><span class="pre">prepare_custom_config</span></code> is None, an empty <code class="docutils literal notranslate"><span class="pre">PrepareCustomConfig</span></code> will be used.
@@ -736,7 +736,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_name">
-<span class="sig-name descname"><span class="pre">set_standalone_module_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_standalone_module_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_standalone_module_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_standalone_module_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the configuration for running a standalone module identified by <code class="docutils literal notranslate"><span class="pre">module_name</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">qconfig_mapping</span></code> is None, the parent <code class="docutils literal notranslate"><span class="pre">qconfig_mapping</span></code> will be used instead.
 If <code class="docutils literal notranslate"><span class="pre">prepare_custom_config</span></code> is None, an empty <code class="docutils literal notranslate"><span class="pre">PrepareCustomConfig</span></code> will be used.
@@ -750,7 +750,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">PrepareCustomConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict" title="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry.html b/main/generated/torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry.html
index 8aca4cbbe0a..1db8f94a954 100644
--- a/main/generated/torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry.html
+++ b/main/generated/torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>StandaloneModuleConfigEntry<a class="headerlink" href="#standalonemoduleconfigentry" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">StandaloneModuleConfigEntry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[QConfigMapping]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'tuple[Any,</span> <span class="pre">...]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[PrepareCustomConfig]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[BackendConfig]'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#StandaloneModuleConfigEntry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/fx/custom_config.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">StandaloneModuleConfigEntry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[QConfigMapping]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'tuple[Any,</span> <span class="pre">...]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[PrepareCustomConfig]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[BackendConfig]'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#StandaloneModuleConfigEntry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
diff --git a/main/generated/torch.ao.quantization.generate_numeric_debug_handle.html b/main/generated/torch.ao.quantization.generate_numeric_debug_handle.html
index d827a41ba59..461900a7a3c 100644
--- a/main/generated/torch.ao.quantization.generate_numeric_debug_handle.html
+++ b/main/generated/torch.ao.quantization.generate_numeric_debug_handle.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>generate_numeric_debug_handle<a class="headerlink" href="#generate-numeric-debug-handle" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.generate_numeric_debug_handle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">generate_numeric_debug_handle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/pt2e/_numeric_debugger.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.generate_numeric_debug_handle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">generate_numeric_debug_handle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/_numeric_debugger.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.generate_numeric_debug_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Attach numeric_debug_handle_id for all nodes in the graph module of the given
 ExportedProgram, like conv2d, squeeze, conv1d, etc, except for placeholder.
 Notice that nodes like getattr are out of scope since they are not in the graph.</p>
diff --git a/main/generated/torch.ao.quantization.observer.AffineQuantizedObserverBase.html b/main/generated/torch.ao.quantization.observer.AffineQuantizedObserverBase.html
index c488b85ec7c..aa22f205e50 100644
--- a/main/generated/torch.ao.quantization.observer.AffineQuantizedObserverBase.html
+++ b/main/generated/torch.ao.quantization.observer.AffineQuantizedObserverBase.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>AffineQuantizedObserverBase<a class="headerlink" href="#affinequantizedobserverbase" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.AffineQuantizedObserverBase">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">AffineQuantizedObserverBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mapping_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">granularity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_domain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ZeroPointDomain.INT</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">AffineQuantizedObserverBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mapping_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">granularity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_domain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ZeroPointDomain.INT</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for affine quantization (<a class="reference external" href="https://github.com/pytorch/ao/tree/main/torchao/quantization#affine-quantization">https://github.com/pytorch/ao/tree/main/torchao/quantization#affine-quantization</a>)</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -628,7 +628,7 @@ <h1>AffineQuantizedObserverBase<a class="headerlink" href="#affinequantizedobser
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.AffineQuantizedObserverBase.calculate_qparams">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">calculate_qparams</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase.calculate_qparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.calculate_qparams" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">calculate_qparams</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase.calculate_qparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.calculate_qparams" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate quantization parameter based on the stats attached to the observer module
 and returns a tuple of scale and zero_point Tensor</p>
 <dl class="field-list simple">
@@ -640,7 +640,7 @@ <h1>AffineQuantizedObserverBase<a class="headerlink" href="#affinequantizedobser
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.AffineQuantizedObserverBase.forward">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.forward" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>forward function should take the input tensor
 and updates internal stats and return the original input Tensor</p>
 <dl class="field-list simple">
@@ -652,7 +652,7 @@ <h1>AffineQuantizedObserverBase<a class="headerlink" href="#affinequantizedobser
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.AffineQuantizedObserverBase.with_args">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.with_args" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.with_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper that allows creation of class factories.</p>
 <p>This can be useful when there is a need to create classes with the same
 constructor arguments, but different instances. Can be used in conjunction with
diff --git a/main/generated/torch.ao.quantization.observer.Granularity.html b/main/generated/torch.ao.quantization.observer.Granularity.html
index b1e9eb5b6e4..0ad5be78cac 100644
--- a/main/generated/torch.ao.quantization.observer.Granularity.html
+++ b/main/generated/torch.ao.quantization.observer.Granularity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Granularity<a class="headerlink" href="#granularity" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.Granularity">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">Granularity</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#Granularity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.Granularity" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">Granularity</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#Granularity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.Granularity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for representing the granularity of quantization.</p>
 <p>This class serves as a parent for specific granularity types used in
 quantization operations, such as per-tensor or per-axis quantization.</p>
diff --git a/main/generated/torch.ao.quantization.observer.HistogramObserver.html b/main/generated/torch.ao.quantization.observer.HistogramObserver.html
index b97ed15c43e..cd0ff0c109e 100644
--- a/main/generated/torch.ao.quantization.observer.HistogramObserver.html
+++ b/main/generated/torch.ao.quantization.observer.HistogramObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>HistogramObserver<a class="headerlink" href="#histogramobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.HistogramObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">HistogramObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#HistogramObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L974"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.HistogramObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">HistogramObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#HistogramObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L974"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.HistogramObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>The module records the running histogram of tensor values along with
 min/max values. <code class="docutils literal notranslate"><span class="pre">calculate_qparams</span></code> will calculate scale and zero_point.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.observer.MappingType.html b/main/generated/torch.ao.quantization.observer.MappingType.html
index df714184ba7..f9043ddc8b8 100644
--- a/main/generated/torch.ao.quantization.observer.MappingType.html
+++ b/main/generated/torch.ao.quantization.observer.MappingType.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>MappingType<a class="headerlink" href="#mappingtype" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MappingType">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MappingType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MappingType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MappingType" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MappingType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MappingType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MappingType" title="Permalink to this definition">¶</a></dt>
 <dd><p>How floating point number is mapped to integer number</p>
 <p>symmetric mapping means floating point range is symmetrically mapped to integer range
 let’s say we have floating point range (-3.5, 10.2) and integer range (-8, 7) (int4)
diff --git a/main/generated/torch.ao.quantization.observer.MinMaxObserver.html b/main/generated/torch.ao.quantization.observer.MinMaxObserver.html
index 156c4a6ec70..1ec8d04d27e 100644
--- a/main/generated/torch.ao.quantization.observer.MinMaxObserver.html
+++ b/main/generated/torch.ao.quantization.observer.MinMaxObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>MinMaxObserver<a class="headerlink" href="#minmaxobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MinMaxObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for computing the quantization parameters based on the
 running min and max values.</p>
 <p>This observer uses the tensor min/max statistics to compute the quantization
@@ -674,19 +674,19 @@ <h1>MinMaxObserver<a class="headerlink" href="#minmaxobserver" title="Permalink
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MinMaxObserver.calculate_qparams">
-<span class="sig-name descname"><span class="pre">calculate_qparams</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.calculate_qparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L561"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.calculate_qparams" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">calculate_qparams</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.calculate_qparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L561"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.calculate_qparams" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculates the quantization parameters.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MinMaxObserver.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_orig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_orig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Records the running minimum and maximum of <code class="docutils literal notranslate"><span class="pre">x</span></code>.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MinMaxObserver.reset_min_max_vals">
-<span class="sig-name descname"><span class="pre">reset_min_max_vals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.reset_min_max_vals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.reset_min_max_vals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset_min_max_vals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.reset_min_max_vals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.reset_min_max_vals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Resets the min/max values.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.MovingAverageMinMaxObserver.html b/main/generated/torch.ao.quantization.observer.MovingAverageMinMaxObserver.html
index 2aa69b5762d..a8b956bdab3 100644
--- a/main/generated/torch.ao.quantization.observer.MovingAverageMinMaxObserver.html
+++ b/main/generated/torch.ao.quantization.observer.MovingAverageMinMaxObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>MovingAverageMinMaxObserver<a class="headerlink" href="#movingaverageminmaxobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MovingAverageMinMaxObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MovingAverageMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">averaging_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MovingAverageMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MovingAverageMinMaxObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MovingAverageMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">averaging_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MovingAverageMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MovingAverageMinMaxObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for computing the quantization parameters based on the
 moving average of the min and max values.</p>
 <p>This observer computes the quantization parameters based on the moving
diff --git a/main/generated/torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver.html b/main/generated/torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver.html
index 97f8b06a007..003b26f0954 100644
--- a/main/generated/torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver.html
+++ b/main/generated/torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>MovingAveragePerChannelMinMaxObserver<a class="headerlink" href="#movingaverageperchannelminmaxobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MovingAveragePerChannelMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">averaging_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_channel_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MovingAveragePerChannelMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MovingAveragePerChannelMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">averaging_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_channel_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MovingAveragePerChannelMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for computing the quantization parameters based on the
 running per channel min and max values.</p>
 <p>This observer uses the tensor min/max statistics to compute the per channel
diff --git a/main/generated/torch.ao.quantization.observer.NoopObserver.html b/main/generated/torch.ao.quantization.observer.NoopObserver.html
index e988042748c..1e2f3bd8f6c 100644
--- a/main/generated/torch.ao.quantization.observer.NoopObserver.html
+++ b/main/generated/torch.ao.quantization.observer.NoopObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>NoopObserver<a class="headerlink" href="#noopobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.NoopObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">NoopObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#NoopObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.NoopObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">NoopObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#NoopObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.NoopObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer that doesn’t do anything and just passes its configuration to the
 quantized module’s <code class="docutils literal notranslate"><span class="pre">.from_float()</span></code>.</p>
 <p>Primarily used for quantization to float16 which doesn’t require determining
diff --git a/main/generated/torch.ao.quantization.observer.ObserverBase.html b/main/generated/torch.ao.quantization.observer.ObserverBase.html
index 279b7577869..a69c39ce442 100644
--- a/main/generated/torch.ao.quantization.observer.ObserverBase.html
+++ b/main/generated/torch.ao.quantization.observer.ObserverBase.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ObserverBase<a class="headerlink" href="#observerbase" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.ObserverBase">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">ObserverBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#ObserverBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">ObserverBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#ObserverBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base observer Module.
 Any observer implementation should derive from this class.</p>
 <p>Concrete observers should follow the same API. In forward, they will update
@@ -633,7 +633,7 @@ <h1>ObserverBase<a class="headerlink" href="#observerbase" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.ObserverBase.with_args">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase.with_args" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase.with_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper that allows creation of class factories.</p>
 <p>This can be useful when there is a need to create classes with the same
 constructor arguments, but different instances. Can be used in conjunction with
@@ -651,7 +651,7 @@ <h1>ObserverBase<a class="headerlink" href="#observerbase" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.ObserverBase.with_callable_args">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_callable_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase.with_callable_args" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_callable_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase.with_callable_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper that allows creation of class factories args that need to be
 called at construction time.</p>
 <p>This can be useful when there is a need to create classes with the same
diff --git a/main/generated/torch.ao.quantization.observer.PerAxis.html b/main/generated/torch.ao.quantization.observer.PerAxis.html
index 7ae8aa10fd6..fd144c47896 100644
--- a/main/generated/torch.ao.quantization.observer.PerAxis.html
+++ b/main/generated/torch.ao.quantization.observer.PerAxis.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerAxis<a class="headerlink" href="#peraxis" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerAxis">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerAxis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerAxis"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1699"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerAxis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerAxis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerAxis"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1699"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerAxis" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-axis granularity in quantization.</p>
 <p>This granularity type calculates different quantization parameters
 along a specified axis of the tensor.</p>
diff --git a/main/generated/torch.ao.quantization.observer.PerBlock.html b/main/generated/torch.ao.quantization.observer.PerBlock.html
index b1516f7bb5c..13ab8935a0a 100644
--- a/main/generated/torch.ao.quantization.observer.PerBlock.html
+++ b/main/generated/torch.ao.quantization.observer.PerBlock.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerBlock<a class="headerlink" href="#perblock" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerBlock">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerBlock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">block_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerBlock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerBlock" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerBlock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">block_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerBlock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerBlock" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-block granularity in quantization. See
 <code class="xref py py-func docutils literal notranslate"><span class="pre">quantize_affine()</span></code> for docs for
 <cite>block_size</cite></p>
diff --git a/main/generated/torch.ao.quantization.observer.PerChannelMinMaxObserver.html b/main/generated/torch.ao.quantization.observer.PerChannelMinMaxObserver.html
index dc75f5bb0a7..9cbce330fcc 100644
--- a/main/generated/torch.ao.quantization.observer.PerChannelMinMaxObserver.html
+++ b/main/generated/torch.ao.quantization.observer.PerChannelMinMaxObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerChannelMinMaxObserver<a class="headerlink" href="#perchannelminmaxobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerChannelMinMaxObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerChannelMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ch_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_channel_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerChannelMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L676"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerChannelMinMaxObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerChannelMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ch_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_channel_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerChannelMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L676"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerChannelMinMaxObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for computing the quantization parameters based on the
 running per channel min and max values.</p>
 <p>This observer uses the tensor min/max statistics to compute the per channel
@@ -646,7 +646,7 @@ <h1>PerChannelMinMaxObserver<a class="headerlink" href="#perchannelminmaxobserve
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerChannelMinMaxObserver.reset_min_max_vals">
-<span class="sig-name descname"><span class="pre">reset_min_max_vals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerChannelMinMaxObserver.reset_min_max_vals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L870"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerChannelMinMaxObserver.reset_min_max_vals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset_min_max_vals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerChannelMinMaxObserver.reset_min_max_vals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L870"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerChannelMinMaxObserver.reset_min_max_vals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Resets the min/max values.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.PerGroup.html b/main/generated/torch.ao.quantization.observer.PerGroup.html
index fccf6c986bf..5ffa94a6123 100644
--- a/main/generated/torch.ao.quantization.observer.PerGroup.html
+++ b/main/generated/torch.ao.quantization.observer.PerGroup.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerGroup<a class="headerlink" href="#pergroup" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerGroup">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerGroup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerGroup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerGroup" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerGroup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerGroup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerGroup" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-channel group granularity in quantization.</p>
 <p>This granularity type calculates different quantization parameters
 for each group of &lt;group_size&gt; elements.</p>
diff --git a/main/generated/torch.ao.quantization.observer.PerRow.html b/main/generated/torch.ao.quantization.observer.PerRow.html
index 3cf865110d8..081460d9770 100644
--- a/main/generated/torch.ao.quantization.observer.PerRow.html
+++ b/main/generated/torch.ao.quantization.observer.PerRow.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerRow<a class="headerlink" href="#perrow" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerRow">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerRow</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerRow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1739"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerRow" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerRow</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerRow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1739"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerRow" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents row-wise granularity in quantization.</p>
 <p>This is a special case of per-axis quantization and is unique to Float8 matmuls
 where the input is quantized with a block_size of (1, …, input.shape[-1]). And the weight
diff --git a/main/generated/torch.ao.quantization.observer.PerTensor.html b/main/generated/torch.ao.quantization.observer.PerTensor.html
index b88248affe9..c39a62c4055 100644
--- a/main/generated/torch.ao.quantization.observer.PerTensor.html
+++ b/main/generated/torch.ao.quantization.observer.PerTensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerTensor<a class="headerlink" href="#pertensor" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerTensor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerTensor</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerTensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1688"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerTensor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerTensor</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerTensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1688"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerTensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-tensor granularity in quantization.</p>
 <p>This granularity type calculates the quantization parameters
 based off the entire tensor.</p>
diff --git a/main/generated/torch.ao.quantization.observer.PerToken.html b/main/generated/torch.ao.quantization.observer.PerToken.html
index c8683c4ef1b..ef308bbad8a 100644
--- a/main/generated/torch.ao.quantization.observer.PerToken.html
+++ b/main/generated/torch.ao.quantization.observer.PerToken.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerToken<a class="headerlink" href="#pertoken" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerToken">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerToken</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerToken"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerToken" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerToken</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerToken"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerToken" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-token granularity in quantization.</p>
 <p>This granularity type calculates a different set of quantization parameters
 for each token, which is represented as the last dimension of the tensor.</p>
diff --git a/main/generated/torch.ao.quantization.observer.PlaceholderObserver.html b/main/generated/torch.ao.quantization.observer.PlaceholderObserver.html
index e05091d84b2..dd68296b757 100644
--- a/main/generated/torch.ao.quantization.observer.PlaceholderObserver.html
+++ b/main/generated/torch.ao.quantization.observer.PlaceholderObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PlaceholderObserver<a class="headerlink" href="#placeholderobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PlaceholderObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PlaceholderObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PlaceholderObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PlaceholderObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PlaceholderObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PlaceholderObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PlaceholderObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer that doesn’t do anything and just passes its configuration to the
 quantized module’s <code class="docutils literal notranslate"><span class="pre">.from_float()</span></code>.</p>
 <p>Can be used for quantization to float16 which doesn’t require determining
diff --git a/main/generated/torch.ao.quantization.observer.RecordingObserver.html b/main/generated/torch.ao.quantization.observer.RecordingObserver.html
index c26ff7cd3d5..f25a8b12a3f 100644
--- a/main/generated/torch.ao.quantization.observer.RecordingObserver.html
+++ b/main/generated/torch.ao.quantization.observer.RecordingObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>RecordingObserver<a class="headerlink" href="#recordingobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.RecordingObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">RecordingObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#RecordingObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.RecordingObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">RecordingObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#RecordingObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.RecordingObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>The module is mainly for debug and records the tensor values during runtime.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.observer.TorchAODType.html b/main/generated/torch.ao.quantization.observer.TorchAODType.html
index 1a8cc7111b9..5a2e9bb6385 100644
--- a/main/generated/torch.ao.quantization.observer.TorchAODType.html
+++ b/main/generated/torch.ao.quantization.observer.TorchAODType.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>TorchAODType<a class="headerlink" href="#torchaodtype" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.TorchAODType">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">TorchAODType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#TorchAODType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.TorchAODType" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">TorchAODType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#TorchAODType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.TorchAODType" title="Permalink to this definition">¶</a></dt>
 <dd><p>Placeholder for dtypes that do not exist in PyTorch core yet.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.ZeroPointDomain.html b/main/generated/torch.ao.quantization.observer.ZeroPointDomain.html
index 8a85975091c..a88c0eedd7b 100644
--- a/main/generated/torch.ao.quantization.observer.ZeroPointDomain.html
+++ b/main/generated/torch.ao.quantization.observer.ZeroPointDomain.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ZeroPointDomain<a class="headerlink" href="#zeropointdomain" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.ZeroPointDomain">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">ZeroPointDomain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#ZeroPointDomain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ZeroPointDomain" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">ZeroPointDomain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#ZeroPointDomain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ZeroPointDomain" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enum that indicate whether zero_point is in integer domain or floating point domain</p>
 <p>integer domain: quantized_val = (float_val / scale) (integer) + zero_point (integer)
 float domain: quantized_val = (float_val - (zero_point (float) - scale * mid_point)) / scale
diff --git a/main/generated/torch.ao.quantization.observer.default_debug_observer.html b/main/generated/torch.ao.quantization.observer.default_debug_observer.html
index 9f652bbb540..bd29cabb67c 100644
--- a/main/generated/torch.ao.quantization.observer.default_debug_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_debug_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>default_debug_observer<a class="headerlink" href="#default-debug-observer" title="Permalink to this heading">¶</a></h1>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.default_debug_observer">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">default_debug_observer</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.default_debug_observer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">default_debug_observer</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.default_debug_observer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Default debug-only observer.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.default_dynamic_quant_observer.html b/main/generated/torch.ao.quantization.observer.default_dynamic_quant_observer.html
index eb3ea824439..8208bf64bec 100644
--- a/main/generated/torch.ao.quantization.observer.default_dynamic_quant_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_dynamic_quant_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_float_qparams_observer.html b/main/generated/torch.ao.quantization.observer.default_float_qparams_observer.html
index bd2bb96ab48..0dbc4be1e72 100644
--- a/main/generated/torch.ao.quantization.observer.default_float_qparams_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_float_qparams_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_histogram_observer.html b/main/generated/torch.ao.quantization.observer.default_histogram_observer.html
index cd93811f7c1..bc3c31d96cf 100644
--- a/main/generated/torch.ao.quantization.observer.default_histogram_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_histogram_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_observer.html b/main/generated/torch.ao.quantization.observer.default_observer.html
index 97032ea4529..d2f4dad45d6 100644
--- a/main/generated/torch.ao.quantization.observer.default_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_per_channel_weight_observer.html b/main/generated/torch.ao.quantization.observer.default_per_channel_weight_observer.html
index b0d82327b3c..a5fadd6f1e9 100644
--- a/main/generated/torch.ao.quantization.observer.default_per_channel_weight_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_per_channel_weight_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_placeholder_observer.html b/main/generated/torch.ao.quantization.observer.default_placeholder_observer.html
index 41b3c131905..2c3d072c3f8 100644
--- a/main/generated/torch.ao.quantization.observer.default_placeholder_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_placeholder_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>default_placeholder_observer<a class="headerlink" href="#default-placeholder-observer" title="Permalink to this heading">¶</a></h1>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.default_placeholder_observer">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">default_placeholder_observer</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.default_placeholder_observer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">default_placeholder_observer</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.default_placeholder_observer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Default placeholder observer, usually used for quantization to torch.float16.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.default_weight_observer.html b/main/generated/torch.ao.quantization.observer.default_weight_observer.html
index 88094d2b41d..7e4ed3b9c1d 100644
--- a/main/generated/torch.ao.quantization.observer.default_weight_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_weight_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.get_block_size.html b/main/generated/torch.ao.quantization.observer.get_block_size.html
index 2ede58d925d..41f8a96fd43 100644
--- a/main/generated/torch.ao.quantization.observer.get_block_size.html
+++ b/main/generated/torch.ao.quantization.observer.get_block_size.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>get_block_size<a class="headerlink" href="#get-block-size" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.get_block_size">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">get_block_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">granularity</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#get_block_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1765"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.get_block_size" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">get_block_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">granularity</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#get_block_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1765"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.get_block_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the block size based on the input shape and granularity type.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.observer.get_observer_state_dict.html b/main/generated/torch.ao.quantization.observer.get_observer_state_dict.html
index b3894043de1..6d471ddc857 100644
--- a/main/generated/torch.ao.quantization.observer.get_observer_state_dict.html
+++ b/main/generated/torch.ao.quantization.observer.get_observer_state_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>get_observer_state_dict<a class="headerlink" href="#get-observer-state-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.get_observer_state_dict">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">get_observer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#get_observer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.get_observer_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">get_observer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#get_observer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.get_observer_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the state dict corresponding to the observer stats.
 Traverse the model state_dict and extract out the stats.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.quantization.observer.load_observer_state_dict.html b/main/generated/torch.ao.quantization.observer.load_observer_state_dict.html
index ab13e980942..1d220ff1259 100644
--- a/main/generated/torch.ao.quantization.observer.load_observer_state_dict.html
+++ b/main/generated/torch.ao.quantization.observer.load_observer_state_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>load_observer_state_dict<a class="headerlink" href="#load-observer-state-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.load_observer_state_dict">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">load_observer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#load_observer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/observer.py#L1907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.load_observer_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">load_observer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#load_observer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.load_observer_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given input model and a state_dict containing model observer stats,
 load the stats back into the model. The observer state_dict can be saved
 using torch.ao.quantization.get_observer_state_dict</p>
diff --git a/main/generated/torch.ao.quantization.prepare.html b/main/generated/torch.ao.quantization.prepare.html
index a6d7b5bf145..f56e2b4406e 100644
--- a/main/generated/torch.ao.quantization.prepare.html
+++ b/main/generated/torch.ao.quantization.prepare.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>prepare<a class="headerlink" href="#prepare" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.prepare">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observer_non_leaf_module_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#prepare"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observer_non_leaf_module_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#prepare"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepares a copy of the model for quantization calibration or quantization-aware training.</p>
 <p>Quantization configuration should be assigned preemptively
 to individual submodules in <cite>.qconfig</cite> attribute.</p>
diff --git a/main/generated/torch.ao.quantization.prepare_for_propagation_comparison.html b/main/generated/torch.ao.quantization.prepare_for_propagation_comparison.html
index 2f6edb3fbb8..3cde417b8fa 100644
--- a/main/generated/torch.ao.quantization.prepare_for_propagation_comparison.html
+++ b/main/generated/torch.ao.quantization.prepare_for_propagation_comparison.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>prepare_for_propagation_comparison<a class="headerlink" href="#prepare-for-propagation-comparison" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.prepare_for_propagation_comparison">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare_for_propagation_comparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/pt2e/_numeric_debugger.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare_for_propagation_comparison" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare_for_propagation_comparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/_numeric_debugger.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare_for_propagation_comparison" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add output loggers to node that has numeric_debug_handle</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.prepare_qat.html b/main/generated/torch.ao.quantization.prepare_qat.html
index 1610794b14d..c8ddb39c3f3 100644
--- a/main/generated/torch.ao.quantization.prepare_qat.html
+++ b/main/generated/torch.ao.quantization.prepare_qat.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>prepare_qat<a class="headerlink" href="#prepare-qat" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.prepare_qat">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare_qat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#prepare_qat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare_qat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare_qat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#prepare_qat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare_qat" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepares a copy of the model for quantization calibration or
 quantization-aware training and converts it to quantized version.</p>
 <p>Quantization configuration should be assigned preemptively
diff --git a/main/generated/torch.ao.quantization.propagate_qconfig_.html b/main/generated/torch.ao.quantization.propagate_qconfig_.html
index 6ac5cc6d1ed..e8458bd6d14 100644
--- a/main/generated/torch.ao.quantization.propagate_qconfig_.html
+++ b/main/generated/torch.ao.quantization.propagate_qconfig_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1><a class="reference internal" href="#propagate-qconfig">propagate_qconfig</a><a class="headerlink" href="#propagate-qconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.propagate_qconfig_">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">propagate_qconfig_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#propagate_qconfig_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.propagate_qconfig_" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">propagate_qconfig_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#propagate_qconfig_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.propagate_qconfig_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Propagate qconfig through the module hierarchy and assign <cite>qconfig</cite>
 attribute on each leaf module</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.pt2e.export_utils.model_is_exported.html b/main/generated/torch.ao.quantization.pt2e.export_utils.model_is_exported.html
index c6c70a50827..e3a08af03f7 100644
--- a/main/generated/torch.ao.quantization.pt2e.export_utils.model_is_exported.html
+++ b/main/generated/torch.ao.quantization.pt2e.export_utils.model_is_exported.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>model_is_exported<a class="headerlink" href="#model-is-exported" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.pt2e.export_utils.model_is_exported">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.pt2e.export_utils.</span></span><span class="sig-name descname"><span class="pre">model_is_exported</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/pt2e/export_utils.html#model_is_exported"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/pt2e/export_utils.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.pt2e.export_utils.model_is_exported" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.pt2e.export_utils.</span></span><span class="sig-name descname"><span class="pre">model_is_exported</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/pt2e/export_utils.html#model_is_exported"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/export_utils.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.pt2e.export_utils.model_is_exported" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if the <cite>torch.nn.Module</cite> was exported, False otherwise
 (e.g. if the model was FX symbolically traced or not traced at all).</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.qconfig.QConfig.html b/main/generated/torch.ao.quantization.qconfig.QConfig.html
index 991bfcb56c0..5b5e2f80115 100644
--- a/main/generated/torch.ao.quantization.qconfig.QConfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.QConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>QConfig<a class="headerlink" href="#qconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig.QConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig.</span></span><span class="sig-name descname"><span class="pre">QConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">activation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig.html#QConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig.QConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig.</span></span><span class="sig-name descname"><span class="pre">QConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">activation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig.html#QConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig.QConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Describes how to quantize a layer or a part of the network by providing
 settings (observer classes) for activations and weights respectively.</p>
 <p>Note that QConfig needs to contain observer <strong>classes</strong> (like MinMaxObserver) or a callable that returns
diff --git a/main/generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html
index 3c074d12175..4da7374bf75 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_debug_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_debug_qconfig.html
index 909ea03318a..2ada34deea6 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_debug_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_debug_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html
index 81d305375e4..c8fa87c7b27 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_per_channel_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_per_channel_qconfig.html
index c9f611d0e3b..5ba9d66134b 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_per_channel_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_per_channel_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig.html
index e2c7c031083..c225d2de9c1 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html b/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html
index db97cb63744..874a370267f 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_qconfig.html
index 8b23dff3698..bfa23e7aef7 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_weight_only_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_weight_only_qconfig.html
index 7c091d33fd8..6570df84b67 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_weight_only_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_weight_only_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html b/main/generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html
index 9c089738928..b07fad24aff 100644
--- a/main/generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.float16_static_qconfig.html b/main/generated/torch.ao.quantization.qconfig.float16_static_qconfig.html
index a9fee034de1..e9d2073f26f 100644
--- a/main/generated/torch.ao.quantization.qconfig.float16_static_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.float16_static_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
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diff --git a/main/generated/torch.ao.quantization.qconfig.float_qparams_weight_only_qconfig.html b/main/generated/torch.ao.quantization.qconfig.float_qparams_weight_only_qconfig.html
index 8ef13754423..fc7310e7141 100644
--- a/main/generated/torch.ao.quantization.qconfig.float_qparams_weight_only_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.float_qparams_weight_only_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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diff --git a/main/generated/torch.ao.quantization.qconfig.per_channel_dynamic_qconfig.html b/main/generated/torch.ao.quantization.qconfig.per_channel_dynamic_qconfig.html
index 287d851822b..b6223ece837 100644
--- a/main/generated/torch.ao.quantization.qconfig.per_channel_dynamic_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.per_channel_dynamic_qconfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html b/main/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html
index 0b8f976c8f2..7452c4e45e9 100644
--- a/main/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html
+++ b/main/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">QConfigMapping</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">QConfigMapping</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mapping from model ops to <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.quantization.QConfig</span></code> s.</p>
 <p>The user can specify QConfigs using the following methods (in increasing match priority):</p>
 <blockquote>
@@ -641,7 +641,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">QConfigMapping</span></code> from a dictionary with the following keys (all optional):</p>
 <blockquote>
 <div><p>“” (for global QConfig)</p>
@@ -660,7 +660,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_global">
-<span class="sig-name descname"><span class="pre">set_global</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_global"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_global" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_global</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_global"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_global" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the global (default) QConfig.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -671,7 +671,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name">
-<span class="sig-name descname"><span class="pre">set_module_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_module_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the QConfig for modules matching the given module name.
 If the QConfig for an existing module name was already set, the new QConfig will override the old one.</p>
 <dl class="field-list simple">
@@ -683,7 +683,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_object_type_order">
-<span class="sig-name descname"><span class="pre">set_module_name_object_type_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name_object_type_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_object_type_order" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_module_name_object_type_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name_object_type_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_object_type_order" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the QConfig for modules matching a combination of the given module name, object type,
 and the index at which the module appears.</p>
 <p>If the QConfig for an existing (module name, object type, index)  was already set, the new QConfig
@@ -697,7 +697,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_regex">
-<span class="sig-name descname"><span class="pre">set_module_name_regex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name_regex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name_regex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_regex" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_module_name_regex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name_regex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name_regex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_regex" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the QConfig for modules matching the given regex string.</p>
 <p>Regexes will be matched in the order in which they are registered through this method.
 Thus, the caller should register more specific patterns first, e.g.:</p>
@@ -720,7 +720,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_object_type">
-<span class="sig-name descname"><span class="pre">set_object_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_object_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_object_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_object_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_object_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_object_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the QConfig for a given module type, function, or method name.
 If the QConfig for an existing object type was already set, the new QConfig will override the old one.</p>
 <dl class="field-list simple">
@@ -732,7 +732,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">QConfigMapping</span></code> to a dictionary with the following keys:</p>
 <blockquote>
 <div><p>“” (for global QConfig)</p>
diff --git a/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html b/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html
index 3a71293f210..edbbc6332b3 100644
--- a/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html
+++ b/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>get_default_qat_qconfig_mapping<a class="headerlink" href="#get-default-qat-qconfig-mapping" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">get_default_qat_qconfig_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x86'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#get_default_qat_qconfig_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">get_default_qat_qconfig_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x86'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#get_default_qat_qconfig_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the default QConfigMapping for quantization aware training.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping.html b/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping.html
index 0c86fab8b21..432b5e9a455 100644
--- a/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping.html
+++ b/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>get_default_qconfig_mapping<a class="headerlink" href="#get-default-qconfig-mapping" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">get_default_qconfig_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x86'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#get_default_qconfig_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/qconfig_mapping.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">get_default_qconfig_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x86'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#get_default_qconfig_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the default QConfigMapping for post training quantization.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.quantize.html b/main/generated/torch.ao.quantization.quantize.html
index 5cd7daa0cc5..8183168b420 100644
--- a/main/generated/torch.ao.quantization.quantize.html
+++ b/main/generated/torch.ao.quantization.quantize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>quantize<a class="headerlink" href="#quantize" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Quantize the input float model with post training static quantization.</p>
 <p>First it will prepare the model for calibration, then it calls
 <cite>run_fn</cite> which will run the calibration step, after that we will
diff --git a/main/generated/torch.ao.quantization.quantize_dynamic.html b/main/generated/torch.ao.quantization.quantize_dynamic.html
index 24f30f5d571..329736f3891 100644
--- a/main/generated/torch.ao.quantization.quantize_dynamic.html
+++ b/main/generated/torch.ao.quantization.quantize_dynamic.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>quantize_dynamic<a class="headerlink" href="#quantize-dynamic" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_dynamic">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize_dynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize_dynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize.py#L474"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_dynamic" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize_dynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize_dynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L474"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_dynamic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts a float model to dynamic (i.e. weights-only) quantized model.</p>
 <p>Replaces specified modules with dynamic weight-only quantized versions and output the quantized model.</p>
 <p>For simplest usage provide <cite>dtype</cite> argument that can be float16 or qint8. Weight-only quantization
diff --git a/main/generated/torch.ao.quantization.quantize_fx.convert_fx.html b/main/generated/torch.ao.quantization.quantize_fx.convert_fx.html
index 17471550c5d..79c305f7ef1 100644
--- a/main/generated/torch.ao.quantization.quantize_fx.convert_fx.html
+++ b/main/generated/torch.ao.quantization.quantize_fx.convert_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>convert_fx<a class="headerlink" href="#convert-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_fx.convert_fx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">convert_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convert_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_remove_qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_original_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#convert_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize_fx.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.convert_fx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">convert_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convert_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_remove_qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_original_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#convert_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize_fx.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.convert_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a calibrated or trained model to a quantized model</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.quantize_fx.fuse_fx.html b/main/generated/torch.ao.quantization.quantize_fx.fuse_fx.html
index b808b099440..6bd0a83ebb5 100644
--- a/main/generated/torch.ao.quantization.quantize_fx.fuse_fx.html
+++ b/main/generated/torch.ao.quantization.quantize_fx.fuse_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>fuse_fx<a class="headerlink" href="#fuse-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_fx.fuse_fx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">fuse_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuse_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#fuse_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize_fx.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.fuse_fx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">fuse_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuse_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#fuse_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize_fx.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.fuse_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse modules like conv+bn, conv+bn+relu etc, model must be in eval mode.
 Fusion rules are defined in torch.ao.quantization.fx.fusion_pattern.py</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.quantize_fx.prepare_fx.html b/main/generated/torch.ao.quantization.quantize_fx.prepare_fx.html
index e58d45e3932..a475c1f8bd0 100644
--- a/main/generated/torch.ao.quantization.quantize_fx.prepare_fx.html
+++ b/main/generated/torch.ao.quantization.quantize_fx.prepare_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>prepare_fx<a class="headerlink" href="#prepare-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_fx.prepare_fx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_equalization_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#prepare_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize_fx.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.prepare_fx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_equalization_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#prepare_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize_fx.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.prepare_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepare a model for post training quantization</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.quantize_fx.prepare_qat_fx.html b/main/generated/torch.ao.quantization.quantize_fx.prepare_qat_fx.html
index 13eee1e3097..009bdc03348 100644
--- a/main/generated/torch.ao.quantization.quantize_fx.prepare_qat_fx.html
+++ b/main/generated/torch.ao.quantization.quantize_fx.prepare_qat_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>prepare_qat_fx<a class="headerlink" href="#prepare-qat-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_fx.prepare_qat_fx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_qat_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#prepare_qat_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize_fx.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.prepare_qat_fx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_qat_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#prepare_qat_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize_fx.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.prepare_qat_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepare a model for quantization aware training</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.quantize_qat.html b/main/generated/torch.ao.quantization.quantize_qat.html
index d2983ec6f26..83c9fd1ac39 100644
--- a/main/generated/torch.ao.quantization.quantize_qat.html
+++ b/main/generated/torch.ao.quantization.quantize_qat.html
@@ -300,7 +300,7 @@
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>quantize_qat<a class="headerlink" href="#quantize-qat" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_qat">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize_qat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize_qat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_qat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize_qat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize_qat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_qat" title="Permalink to this definition">¶</a></dt>
 <dd><p>Do quantization aware training and output a quantized model</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.swap_module.html b/main/generated/torch.ao.quantization.swap_module.html
index 619d97b1f0b..e26ba157245 100644
--- a/main/generated/torch.ao.quantization.swap_module.html
+++ b/main/generated/torch.ao.quantization.swap_module.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>swap_module<a class="headerlink" href="#swap-module" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.swap_module">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">swap_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_module_class_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#swap_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/quantization/quantize.py#L730"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.swap_module" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">swap_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_module_class_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#swap_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L730"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.swap_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Swaps the module if it has a quantized counterpart and it has an
 <cite>observer</cite> attached.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.arange.html b/main/generated/torch.arange.html
index f21010ccf1d..06001de65f0 100644
--- a/main/generated/torch.arange.html
+++ b/main/generated/torch.arange.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arccos.html b/main/generated/torch.arccos.html
index d4263214d15..ebe791f1379 100644
--- a/main/generated/torch.arccos.html
+++ b/main/generated/torch.arccos.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arccosh.html b/main/generated/torch.arccosh.html
index 2c47efd7455..c4e90226f9c 100644
--- a/main/generated/torch.arccosh.html
+++ b/main/generated/torch.arccosh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arcsin.html b/main/generated/torch.arcsin.html
index 44deb97236e..40a9ba7cfc7 100644
--- a/main/generated/torch.arcsin.html
+++ b/main/generated/torch.arcsin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arcsinh.html b/main/generated/torch.arcsinh.html
index 26b81f39eae..947966f993f 100644
--- a/main/generated/torch.arcsinh.html
+++ b/main/generated/torch.arcsinh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arctan.html b/main/generated/torch.arctan.html
index 1cbb4bc21b1..fef463de43d 100644
--- a/main/generated/torch.arctan.html
+++ b/main/generated/torch.arctan.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arctan2.html b/main/generated/torch.arctan2.html
index 45c6a92d1b7..89a718996ed 100644
--- a/main/generated/torch.arctan2.html
+++ b/main/generated/torch.arctan2.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arctanh.html b/main/generated/torch.arctanh.html
index 7979a38dfe9..4c5c501afe1 100644
--- a/main/generated/torch.arctanh.html
+++ b/main/generated/torch.arctanh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/generated/torch.are_deterministic_algorithms_enabled.html b/main/generated/torch.are_deterministic_algorithms_enabled.html
index 2e505860b69..4722cabfa96 100644
--- a/main/generated/torch.are_deterministic_algorithms_enabled.html
+++ b/main/generated/torch.are_deterministic_algorithms_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.are_deterministic_algorithms_enabled<a class="headerlink" href="#torch-are-deterministic-algorithms-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.are_deterministic_algorithms_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">are_deterministic_algorithms_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#are_deterministic_algorithms_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.are_deterministic_algorithms_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">are_deterministic_algorithms_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#are_deterministic_algorithms_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.are_deterministic_algorithms_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if the global deterministic flag is turned on. Refer to
 <a class="reference internal" href="torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a> documentation for more details.</p>
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 <h1>torch.atleast_1d<a class="headerlink" href="#torch-atleast-1d" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.atleast_1d">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L1493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a 1-dimensional view of each input tensor with zero dimensions.
 Input tensors with one or more dimensions are returned as-is.</p>
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diff --git a/main/generated/torch.atleast_2d.html b/main/generated/torch.atleast_2d.html
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 <h1>torch.atleast_2d<a class="headerlink" href="#torch-atleast-2d" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.atleast_2d">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L1529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a 2-dimensional view of each input tensor with zero dimensions.
 Input tensors with two or more dimensions are returned as-is.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.atleast_3d.html b/main/generated/torch.atleast_3d.html
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@@ -612,7 +612,7 @@
 <h1>torch.atleast_3d<a class="headerlink" href="#torch-atleast-3d" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.atleast_3d">
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+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a 3-dimensional view of each input tensor with zero dimensions.
 Input tensors with three or more dimensions are returned as-is.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.autograd.Function.backward.html b/main/generated/torch.autograd.Function.backward.html
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 <h1>torch.autograd.Function.backward<a class="headerlink" href="#torch-autograd-function-backward" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.Function.backward">
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+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with backward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
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diff --git a/main/generated/torch.autograd.Function.forward.html b/main/generated/torch.autograd.Function.forward.html
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 <dt class="sig sig-object py" id="torch.autograd.Function.forward">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.forward" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the forward of the custom autograd Function.</p>
 <p>This function is to be overridden by all subclasses.
 There are two ways to define forward:</p>
diff --git a/main/generated/torch.autograd.Function.jvp.html b/main/generated/torch.autograd.Function.jvp.html
index 87d90ff0a92..6403a7116ce 100644
--- a/main/generated/torch.autograd.Function.jvp.html
+++ b/main/generated/torch.autograd.Function.jvp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.Function.jvp<a class="headerlink" href="#torch-autograd-function-jvp" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.Function.jvp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.jvp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with forward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by
diff --git a/main/generated/torch.autograd.Function.vmap.html b/main/generated/torch.autograd.Function.vmap.html
index 33003d16a04..c299cdcc496 100644
--- a/main/generated/torch.autograd.Function.vmap.html
+++ b/main/generated/torch.autograd.Function.vmap.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.Function.vmap<a class="headerlink" href="#torch-autograd-function-vmap" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.Function.vmap">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#Function.vmap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.vmap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#Function.vmap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the behavior for this autograd.Function underneath <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>.</p>
 <p>For a <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.Function()</span></code></a> to support
 <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>, you must either override this static method, or set
diff --git a/main/generated/torch.autograd.backward.html b/main/generated/torch.autograd.backward.html
index 4085f37c4e4..b7741c3d139 100644
--- a/main/generated/torch.autograd.backward.html
+++ b/main/generated/torch.autograd.backward.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.backward<a class="headerlink" href="#torch-autograd-backward" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.backward">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_variables</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd.html#backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/__init__.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_variables</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd.html#backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/__init__.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the sum of gradients of given tensors with respect to graph leaves.</p>
 <p>The graph is differentiated using the chain rule. If any of <code class="docutils literal notranslate"><span class="pre">tensors</span></code>
 are non-scalar (i.e. their data has more than one element) and require
diff --git a/main/generated/torch.autograd.forward_ad.UnpackedDualTensor.html b/main/generated/torch.autograd.forward_ad.UnpackedDualTensor.html
index 2722832c782..e524d1637ed 100644
--- a/main/generated/torch.autograd.forward_ad.UnpackedDualTensor.html
+++ b/main/generated/torch.autograd.forward_ad.UnpackedDualTensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UnpackedDualTensor<a class="headerlink" href="#unpackeddualtensor" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.UnpackedDualTensor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">UnpackedDualTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">primal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangent</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#UnpackedDualTensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/forward_ad.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.UnpackedDualTensor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">UnpackedDualTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">primal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangent</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#UnpackedDualTensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.UnpackedDualTensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Namedtuple returned by <a class="reference internal" href="torch.autograd.forward_ad.unpack_dual.html#torch.autograd.forward_ad.unpack_dual" title="torch.autograd.forward_ad.unpack_dual"><code class="xref py py-func docutils literal notranslate"><span class="pre">unpack_dual()</span></code></a> containing the primal and tangent components of the dual tensor.</p>
 <p>See <a class="reference internal" href="torch.autograd.forward_ad.unpack_dual.html#torch.autograd.forward_ad.unpack_dual" title="torch.autograd.forward_ad.unpack_dual"><code class="xref py py-func docutils literal notranslate"><span class="pre">unpack_dual()</span></code></a> for more details.</p>
 <dl class="py method">
diff --git a/main/generated/torch.autograd.forward_ad.dual_level.html b/main/generated/torch.autograd.forward_ad.dual_level.html
index c389419e8e2..70bfa2ef736 100644
--- a/main/generated/torch.autograd.forward_ad.dual_level.html
+++ b/main/generated/torch.autograd.forward_ad.dual_level.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>dual_level<a class="headerlink" href="#dual-level" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.dual_level">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">dual_level</span></span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/forward_ad.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.dual_level" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">dual_level</span></span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.dual_level" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager for forward AD, where all forward AD computation must occur within the <code class="docutils literal notranslate"><span class="pre">dual_level</span></code> context.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.autograd.forward_ad.enter_dual_level.html b/main/generated/torch.autograd.forward_ad.enter_dual_level.html
index 99a663fd692..6e81db86ea0 100644
--- a/main/generated/torch.autograd.forward_ad.enter_dual_level.html
+++ b/main/generated/torch.autograd.forward_ad.enter_dual_level.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.forward_ad.enter_dual_level<a class="headerlink" href="#torch-autograd-forward-ad-enter-dual-level" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.enter_dual_level">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">enter_dual_level</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#enter_dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/forward_ad.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.enter_dual_level" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">enter_dual_level</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#enter_dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.enter_dual_level" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enter a new forward grad level.</p>
 <p>This level can be used to make and unpack dual Tensors to compute
 forward gradients.</p>
diff --git a/main/generated/torch.autograd.forward_ad.exit_dual_level.html b/main/generated/torch.autograd.forward_ad.exit_dual_level.html
index 24c70595c2b..eb9deb770d7 100644
--- a/main/generated/torch.autograd.forward_ad.exit_dual_level.html
+++ b/main/generated/torch.autograd.forward_ad.exit_dual_level.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.forward_ad.exit_dual_level<a class="headerlink" href="#torch-autograd-forward-ad-exit-dual-level" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.exit_dual_level">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">exit_dual_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#exit_dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/forward_ad.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.exit_dual_level" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">exit_dual_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#exit_dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.exit_dual_level" title="Permalink to this definition">¶</a></dt>
 <dd><p>Exit a forward grad level.</p>
 <p>This function deletes all the gradients associated with this
 level. Only deleting the latest entered level is allowed.</p>
diff --git a/main/generated/torch.autograd.forward_ad.make_dual.html b/main/generated/torch.autograd.forward_ad.make_dual.html
index 4b730afd9e5..7b0155b3717 100644
--- a/main/generated/torch.autograd.forward_ad.make_dual.html
+++ b/main/generated/torch.autograd.forward_ad.make_dual.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.forward_ad.make_dual<a class="headerlink" href="#torch-autograd-forward-ad-make-dual" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.make_dual">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">make_dual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangent</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#make_dual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/forward_ad.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.make_dual" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">make_dual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangent</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#make_dual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.make_dual" title="Permalink to this definition">¶</a></dt>
 <dd><p>Associate a tensor value with its tangent to create a “dual tensor” for forward AD gradient computation.</p>
 <p>The result is a new tensor aliased to <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code> with <code class="xref py py-attr docutils literal notranslate"><span class="pre">tangent</span></code> embedded
 as an attribute as-is if it has the same storage layout or copied otherwise.
diff --git a/main/generated/torch.autograd.forward_ad.unpack_dual.html b/main/generated/torch.autograd.forward_ad.unpack_dual.html
index 11669a6facc..817299c3819 100644
--- a/main/generated/torch.autograd.forward_ad.unpack_dual.html
+++ b/main/generated/torch.autograd.forward_ad.unpack_dual.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.forward_ad.unpack_dual<a class="headerlink" href="#torch-autograd-forward-ad-unpack-dual" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.unpack_dual">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">unpack_dual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#unpack_dual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/forward_ad.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.unpack_dual" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">unpack_dual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#unpack_dual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.unpack_dual" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unpack a “dual tensor” to get both its Tensor value and its forward AD gradient.</p>
 <p>The result is a namedtuple <code class="docutils literal notranslate"><span class="pre">(primal,</span> <span class="pre">tangent)</span></code> where <code class="docutils literal notranslate"><span class="pre">primal</span></code> is a view of
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code>’s primal and <code class="docutils literal notranslate"><span class="pre">tangent</span></code> is <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code>’s tangent as-is.
diff --git a/main/generated/torch.autograd.function.BackwardCFunction.html b/main/generated/torch.autograd.function.BackwardCFunction.html
index 26347b4545c..3e22854ddf2 100644
--- a/main/generated/torch.autograd.function.BackwardCFunction.html
+++ b/main/generated/torch.autograd.function.BackwardCFunction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,23 +612,23 @@
 <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">BackwardCFunction</span></span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">BackwardCFunction</span></span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction" title="Permalink to this definition">¶</a></dt>
 <dd><p>This class is used for internal autograd work. Do not use.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply method used when executing this Node during the backward</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.apply_jvp">
-<span class="sig-name descname"><span class="pre">apply_jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction.apply_jvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.apply_jvp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction.apply_jvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.apply_jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply method used when executing forward mode AD during the forward</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.mark_dirty">
-<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark given tensors as modified in an in-place operation.</p>
 <p>This should be called at most once, in either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code>
 or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments should be inputs.</p>
@@ -666,7 +666,7 @@ <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.mark_non_differentiable">
-<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark outputs as non-differentiable.</p>
 <p>This should be called at most once, in either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code>
 or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments should be tensor outputs.</p>
@@ -701,7 +701,7 @@ <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.save_for_backward">
-<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_backward</span></code> should be called at most once, in either the
 <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and only with tensors.</p>
@@ -758,7 +758,7 @@ <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.save_for_forward">
-<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_forward</span></code> should be called at most once, in either the
 <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments
@@ -806,7 +806,7 @@ <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.set_materialize_grads">
-<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether to materialize grad tensors. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <p>This should be called only from either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or
 <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods.</p>
diff --git a/main/generated/torch.autograd.function.FunctionCtx.mark_dirty.html b/main/generated/torch.autograd.function.FunctionCtx.mark_dirty.html
index fb82b1a32ad..f41c8c8d8df 100644
--- a/main/generated/torch.autograd.function.FunctionCtx.mark_dirty.html
+++ b/main/generated/torch.autograd.function.FunctionCtx.mark_dirty.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.FunctionCtx.mark_dirty<a class="headerlink" href="#torch-autograd-function-functionctx-mark-dirty" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.FunctionCtx.mark_dirty">
-<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.mark_dirty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.mark_dirty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.mark_dirty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.mark_dirty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark given tensors as modified in an in-place operation.</p>
 <p>This should be called at most once, in either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code>
 or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments should be inputs.</p>
diff --git a/main/generated/torch.autograd.function.FunctionCtx.mark_non_differentiable.html b/main/generated/torch.autograd.function.FunctionCtx.mark_non_differentiable.html
index b58b23e670b..0a573d93309 100644
--- a/main/generated/torch.autograd.function.FunctionCtx.mark_non_differentiable.html
+++ b/main/generated/torch.autograd.function.FunctionCtx.mark_non_differentiable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.FunctionCtx.mark_non_differentiable<a class="headerlink" href="#torch-autograd-function-functionctx-mark-non-differentiable" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.FunctionCtx.mark_non_differentiable">
-<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.mark_non_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.mark_non_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark outputs as non-differentiable.</p>
 <p>This should be called at most once, in either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code>
 or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments should be tensor outputs.</p>
diff --git a/main/generated/torch.autograd.function.FunctionCtx.save_for_backward.html b/main/generated/torch.autograd.function.FunctionCtx.save_for_backward.html
index 923215cf865..09b74db078f 100644
--- a/main/generated/torch.autograd.function.FunctionCtx.save_for_backward.html
+++ b/main/generated/torch.autograd.function.FunctionCtx.save_for_backward.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.FunctionCtx.save_for_backward<a class="headerlink" href="#torch-autograd-function-functionctx-save-for-backward" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.FunctionCtx.save_for_backward">
-<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.save_for_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.save_for_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.save_for_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.save_for_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_backward</span></code> should be called at most once, in either the
 <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and only with tensors.</p>
diff --git a/main/generated/torch.autograd.function.FunctionCtx.set_materialize_grads.html b/main/generated/torch.autograd.function.FunctionCtx.set_materialize_grads.html
index 5c97c19a741..b951c25a7e0 100644
--- a/main/generated/torch.autograd.function.FunctionCtx.set_materialize_grads.html
+++ b/main/generated/torch.autograd.function.FunctionCtx.set_materialize_grads.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.FunctionCtx.set_materialize_grads<a class="headerlink" href="#torch-autograd-function-functionctx-set-materialize-grads" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.FunctionCtx.set_materialize_grads">
-<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.set_materialize_grads"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.set_materialize_grads"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether to materialize grad tensors. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <p>This should be called only from either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or
 <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods.</p>
diff --git a/main/generated/torch.autograd.function.InplaceFunction.html b/main/generated/torch.autograd.function.InplaceFunction.html
index 93354ea1af1..778695ad910 100644
--- a/main/generated/torch.autograd.function.InplaceFunction.html
+++ b/main/generated/torch.autograd.function.InplaceFunction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">InplaceFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#InplaceFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">InplaceFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#InplaceFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction" title="Permalink to this definition">¶</a></dt>
 <dd><p>This class is here only for backward compatibility reasons.
 Use <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.function.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">Function</span></code></a> instead of this for any new use case.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.backward">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.backward" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with backward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 (Defining this function is equivalent to defining the <code class="docutils literal notranslate"><span class="pre">vjp</span></code> function.)</p>
@@ -644,7 +644,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.forward">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.forward" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the forward of the custom autograd Function.</p>
 <p>This function is to be overridden by all subclasses.
 There are two ways to define forward:</p>
@@ -693,7 +693,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.jvp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.jvp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with forward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by
@@ -716,7 +716,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.mark_dirty">
-<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark given tensors as modified in an in-place operation.</p>
 <p>This should be called at most once, in either the <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a>
 or <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and all arguments should be inputs.</p>
@@ -754,7 +754,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.mark_non_differentiable">
-<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark outputs as non-differentiable.</p>
 <p>This should be called at most once, in either the <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a>
 or <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and all arguments should be tensor outputs.</p>
@@ -789,7 +789,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.save_for_backward">
-<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_backward</span></code> should be called at most once, in either the
 <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and only with tensors.</p>
@@ -846,7 +846,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.save_for_forward">
-<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_forward</span></code> should be called at most once, in either the
 <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and all arguments
@@ -894,7 +894,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.set_materialize_grads">
-<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether to materialize grad tensors. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <p>This should be called only from either the <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or
 <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods.</p>
@@ -942,7 +942,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.setup_context">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">setup_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.setup_context" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">setup_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.setup_context" title="Permalink to this definition">¶</a></dt>
 <dd><p>There are two ways to define the forward pass of an autograd.Function.</p>
 <p>Either:</p>
 <ol class="arabic simple">
@@ -963,7 +963,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.vjp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.vjp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.vjp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with backward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 (Defining this function is equivalent to defining the <code class="docutils literal notranslate"><span class="pre">vjp</span></code> function.)</p>
@@ -990,7 +990,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.vmap">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.vmap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the behavior for this autograd.Function underneath <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>.</p>
 <p>For a <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.Function()</span></code></a> to support
 <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>, you must either override this static method, or set
diff --git a/main/generated/torch.autograd.function.NestedIOFunction.html b/main/generated/torch.autograd.function.NestedIOFunction.html
index 68b2cf42a5b..31cec81d970 100644
--- a/main/generated/torch.autograd.function.NestedIOFunction.html
+++ b/main/generated/torch.autograd.function.NestedIOFunction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">NestedIOFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L766"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">NestedIOFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L766"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction" title="Permalink to this definition">¶</a></dt>
 <dd><p>This class is here only for backward compatibility reasons.
 Use <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.function.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">Function</span></code></a> instead of this for any new use case.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.backward">
-<span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">gradients</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L789"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">gradients</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L789"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Shared backward utility.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -628,7 +628,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.backward_extended">
-<span class="sig-name descname"><span class="pre">backward_extended</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_output</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.backward_extended"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.backward_extended" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">backward_extended</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_output</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.backward_extended"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.backward_extended" title="Permalink to this definition">¶</a></dt>
 <dd><p>User defined backward.</p>
 <dl class="field-list simple">
 </dl>
@@ -636,7 +636,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Shared forward utility.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -647,7 +647,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.forward_extended">
-<span class="sig-name descname"><span class="pre">forward_extended</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.forward_extended"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.forward_extended" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward_extended</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.forward_extended"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.forward_extended" title="Permalink to this definition">¶</a></dt>
 <dd><p>User defined forward.</p>
 <dl class="field-list simple">
 </dl>
@@ -655,7 +655,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.jvp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.jvp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with forward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by
@@ -678,7 +678,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.mark_dirty">
-<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.mark_dirty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.mark_dirty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">Function.mark_dirty()</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -686,7 +686,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.mark_non_differentiable">
-<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.mark_non_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.mark_non_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">Function.mark_non_differentiable()</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -694,7 +694,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.save_for_backward">
-<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.save_for_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.save_for_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">Function.save_for_backward()</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -702,7 +702,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.save_for_forward">
-<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_forward</span></code> should be called at most once, in either the
 <a class="reference internal" href="#torch.autograd.function.NestedIOFunction.setup_context" title="torch.autograd.function.NestedIOFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or <a class="reference internal" href="#torch.autograd.function.NestedIOFunction.forward" title="torch.autograd.function.NestedIOFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and all arguments
@@ -756,7 +756,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.set_materialize_grads">
-<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether to materialize grad tensors. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <p>This should be called only from either the <a class="reference internal" href="#torch.autograd.function.NestedIOFunction.setup_context" title="torch.autograd.function.NestedIOFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or
 <a class="reference internal" href="#torch.autograd.function.NestedIOFunction.forward" title="torch.autograd.function.NestedIOFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods.</p>
@@ -804,7 +804,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.setup_context">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">setup_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.setup_context" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">setup_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.setup_context" title="Permalink to this definition">¶</a></dt>
 <dd><p>There are two ways to define the forward pass of an autograd.Function.</p>
 <p>Either:</p>
 <ol class="arabic simple">
@@ -825,7 +825,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.vjp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.vjp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.vjp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with backward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 (Defining this function is equivalent to defining the <code class="docutils literal notranslate"><span class="pre">vjp</span></code> function.)</p>
@@ -852,7 +852,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.vmap">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.vmap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the behavior for this autograd.Function underneath <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>.</p>
 <p>For a <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.Function()</span></code></a> to support
 <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>, you must either override this static method, or set
diff --git a/main/generated/torch.autograd.function.once_differentiable.html b/main/generated/torch.autograd.function.once_differentiable.html
index ab196fb6e11..54a8036a4f0 100644
--- a/main/generated/torch.autograd.function.once_differentiable.html
+++ b/main/generated/torch.autograd.function.once_differentiable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.once_differentiable<a class="headerlink" href="#torch-autograd-function-once-differentiable" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.function.once_differentiable">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">once_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#once_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/function.py#L599"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.once_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">once_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#once_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L599"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.once_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.autograd.functional.hessian.html b/main/generated/torch.autograd.functional.hessian.html
index af1aed55392..70c2f28e09c 100644
--- a/main/generated/torch.autograd.functional.hessian.html
+++ b/main/generated/torch.autograd.functional.hessian.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.hessian<a class="headerlink" href="#torch-autograd-functional-hessian" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.hessian">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">hessian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outer_jacobian_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reverse-mode'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#hessian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/functional.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.hessian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">hessian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outer_jacobian_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reverse-mode'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#hessian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.hessian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the Hessian of a given scalar function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.hvp.html b/main/generated/torch.autograd.functional.hvp.html
index 30470d01a92..c070b6a7fe1 100644
--- a/main/generated/torch.autograd.functional.hvp.html
+++ b/main/generated/torch.autograd.functional.hvp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.hvp<a class="headerlink" href="#torch-autograd-functional-hvp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.hvp">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">hvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#hvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/functional.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.hvp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">hvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#hvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.hvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the dot product between the scalar function’s Hessian and a vector <code class="docutils literal notranslate"><span class="pre">v</span></code> at a specified point.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.jacobian.html b/main/generated/torch.autograd.functional.jacobian.html
index 92160882591..92144c081fb 100644
--- a/main/generated/torch.autograd.functional.jacobian.html
+++ b/main/generated/torch.autograd.functional.jacobian.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.jacobian<a class="headerlink" href="#torch-autograd-functional-jacobian" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.jacobian">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">jacobian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reverse-mode'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#jacobian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/functional.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.jacobian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">jacobian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reverse-mode'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#jacobian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.jacobian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the Jacobian of a given function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.jvp.html b/main/generated/torch.autograd.functional.jvp.html
index 829384e6b7b..16ba89673c8 100644
--- a/main/generated/torch.autograd.functional.jvp.html
+++ b/main/generated/torch.autograd.functional.jvp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.jvp<a class="headerlink" href="#torch-autograd-functional-jvp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.jvp">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#jvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/functional.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.jvp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#jvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the dot product between the Jacobian of the given function at the point given by the inputs and a vector <code class="docutils literal notranslate"><span class="pre">v</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.vhp.html b/main/generated/torch.autograd.functional.vhp.html
index 83b00d6cb04..1501c2147e4 100644
--- a/main/generated/torch.autograd.functional.vhp.html
+++ b/main/generated/torch.autograd.functional.vhp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.vhp<a class="headerlink" href="#torch-autograd-functional-vhp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.vhp">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">vhp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#vhp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/functional.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.vhp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">vhp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#vhp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.vhp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the dot product between vector <code class="docutils literal notranslate"><span class="pre">v</span></code> and Hessian of a  given scalar function at a specified point.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.vjp.html b/main/generated/torch.autograd.functional.vjp.html
index 86744e5a89d..292ac5a9cbc 100644
--- a/main/generated/torch.autograd.functional.vjp.html
+++ b/main/generated/torch.autograd.functional.vjp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.vjp<a class="headerlink" href="#torch-autograd-functional-vjp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.vjp">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#vjp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/functional.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.vjp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#vjp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.vjp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the dot product between a vector <code class="docutils literal notranslate"><span class="pre">v</span></code> and the Jacobian of the given function at the point given by the inputs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.grad.html b/main/generated/torch.autograd.grad.html
index 518f62e8752..0e672bae28b 100644
--- a/main/generated/torch.autograd.grad.html
+++ b/main/generated/torch.autograd.grad.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.grad<a class="headerlink" href="#torch-autograd-grad" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.grad">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">only_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_grads_batched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">materialize_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd.html#grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/__init__.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">only_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_grads_batched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">materialize_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd.html#grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/__init__.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and return the sum of gradients of outputs with respect to the inputs.</p>
 <p><code class="docutils literal notranslate"><span class="pre">grad_outputs</span></code> should be a sequence of length matching <code class="docutils literal notranslate"><span class="pre">output</span></code>
 containing the “vector” in vector-Jacobian product, usually the pre-computed
diff --git a/main/generated/torch.autograd.grad_mode.inference_mode.html b/main/generated/torch.autograd.grad_mode.inference_mode.html
index 37178c148e8..41ecc923c0c 100644
--- a/main/generated/torch.autograd.grad_mode.inference_mode.html
+++ b/main/generated/torch.autograd.grad_mode.inference_mode.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>inference_mode<a class="headerlink" href="#inference-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.inference_mode">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">inference_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#inference_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/grad_mode.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.inference_mode" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">inference_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#inference_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.inference_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that enables or disables inference mode.</p>
 <p>InferenceMode is a context manager analogous to <a class="reference internal" href="torch.no_grad.html#torch.no_grad" title="torch.autograd.grad_mode.no_grad"><code class="xref py py-class docutils literal notranslate"><span class="pre">no_grad</span></code></a>
 to be used when you are certain your operations will have no interactions
@@ -665,7 +665,7 @@ <h1>inference_mode<a class="headerlink" href="#inference-mode" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.inference_mode.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#inference_mode.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/grad_mode.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.inference_mode.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#inference_mode.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.inference_mode.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a copy of this class</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.autograd.grad_mode.set_grad_enabled.html b/main/generated/torch.autograd.grad_mode.set_grad_enabled.html
index 1bce84cdbfe..4fe2825cf87 100644
--- a/main/generated/torch.autograd.grad_mode.set_grad_enabled.html
+++ b/main/generated/torch.autograd.grad_mode.set_grad_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>set_grad_enabled<a class="headerlink" href="#set-grad-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.set_grad_enabled">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">set_grad_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_grad_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/grad_mode.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_grad_enabled" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">set_grad_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_grad_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_grad_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that sets gradient calculation on or off.</p>
 <p><code class="docutils literal notranslate"><span class="pre">set_grad_enabled</span></code> will enable or disable grads based on its argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">mode</span></code>.
 It can be used as a context-manager or as a function.</p>
@@ -656,7 +656,7 @@ <h1>set_grad_enabled<a class="headerlink" href="#set-grad-enabled" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.set_grad_enabled.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_grad_enabled.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/grad_mode.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_grad_enabled.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_grad_enabled.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_grad_enabled.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a copy of this class</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.autograd.grad_mode.set_multithreading_enabled.html b/main/generated/torch.autograd.grad_mode.set_multithreading_enabled.html
index 7496de93057..942ce7959c9 100644
--- a/main/generated/torch.autograd.grad_mode.set_multithreading_enabled.html
+++ b/main/generated/torch.autograd.grad_mode.set_multithreading_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>set_multithreading_enabled<a class="headerlink" href="#set-multithreading-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.set_multithreading_enabled">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">set_multithreading_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_multithreading_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/grad_mode.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_multithreading_enabled" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">set_multithreading_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_multithreading_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_multithreading_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that sets multithreaded backwards on or off.</p>
 <p><code class="docutils literal notranslate"><span class="pre">set_multithreading_enabled</span></code> will enable or disable multithreaded backwards based on its argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">mode</span></code>.
 It can be used as a context-manager or as a function.</p>
@@ -630,7 +630,7 @@ <h1>set_multithreading_enabled<a class="headerlink" href="#set-multithreading-en
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.set_multithreading_enabled.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_multithreading_enabled.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/grad_mode.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_multithreading_enabled.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_multithreading_enabled.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_multithreading_enabled.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a copy of this class</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.autograd.gradcheck.GradcheckError.html b/main/generated/torch.autograd.gradcheck.GradcheckError.html
index 6b36a1d65a3..d28fee18cc4 100644
--- a/main/generated/torch.autograd.gradcheck.GradcheckError.html
+++ b/main/generated/torch.autograd.gradcheck.GradcheckError.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.gradcheck.GradcheckError<a class="headerlink" href="#torch-autograd-gradcheck-gradcheckerror" title="Permalink to this heading">¶</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="torch.autograd.gradcheck.GradcheckError">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">GradcheckError</span></span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#GradcheckError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/gradcheck.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.GradcheckError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">GradcheckError</span></span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#GradcheckError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/gradcheck.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.GradcheckError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Error raised by <a class="reference internal" href="torch.autograd.gradcheck.gradcheck.html#torch.autograd.gradcheck.gradcheck" title="torch.autograd.gradcheck.gradcheck"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradcheck()</span></code></a> and <a class="reference internal" href="torch.autograd.gradcheck.gradgradcheck.html#torch.autograd.gradcheck.gradgradcheck" title="torch.autograd.gradcheck.gradgradcheck"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradgradcheck()</span></code></a>.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.autograd.gradcheck.gradcheck.html b/main/generated/torch.autograd.gradcheck.gradcheck.html
index 2d082be7b16..5f011b12b63 100644
--- a/main/generated/torch.autograd.gradcheck.gradcheck.html
+++ b/main/generated/torch.autograd.gradcheck.gradcheck.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.gradcheck.gradcheck<a class="headerlink" href="#torch-autograd-gradcheck-gradcheck" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.gradcheck.gradcheck">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">gradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nondet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_undefined_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_grad_dtypes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_forward_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_forward_ad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_backward_ad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">masked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#gradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/gradcheck.py#L1954"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.gradcheck" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">gradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nondet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_undefined_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_grad_dtypes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_forward_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_forward_ad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_backward_ad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">masked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#gradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/gradcheck.py#L1954"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.gradcheck" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check gradients computed via small finite differences against analytical
 gradients wrt tensors in <code class="xref py py-attr docutils literal notranslate"><span class="pre">inputs</span></code> that are of floating point or complex type
 and with <code class="docutils literal notranslate"><span class="pre">requires_grad=True</span></code>.</p>
diff --git a/main/generated/torch.autograd.gradcheck.gradgradcheck.html b/main/generated/torch.autograd.gradcheck.gradgradcheck.html
index 1be1595c3fd..c7fe4b15049 100644
--- a/main/generated/torch.autograd.gradcheck.gradgradcheck.html
+++ b/main/generated/torch.autograd.gradcheck.gradgradcheck.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.gradcheck.gradgradcheck<a class="headerlink" href="#torch-autograd-gradcheck-gradgradcheck" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.gradcheck.gradgradcheck">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">gradgradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gen_non_contig_grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nondet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_undefined_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_grad_dtypes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_fwd_over_rev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_rev_over_rev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">masked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#gradgradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/gradcheck.py#L2119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.gradgradcheck" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">gradgradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gen_non_contig_grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nondet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_undefined_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_grad_dtypes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_fwd_over_rev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_rev_over_rev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">masked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#gradgradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/gradcheck.py#L2119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.gradgradcheck" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check gradients of gradients computed via small finite differences
 against analytical gradients wrt tensors in <code class="xref py py-attr docutils literal notranslate"><span class="pre">inputs</span></code> and
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_outputs</span></code> that are of floating point or complex type and with
diff --git a/main/generated/torch.autograd.graph.Node.metadata.html b/main/generated/torch.autograd.graph.Node.metadata.html
index 1aa9519357f..031cad25e96 100644
--- a/main/generated/torch.autograd.graph.Node.metadata.html
+++ b/main/generated/torch.autograd.graph.Node.metadata.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.Node.metadata<a class="headerlink" href="#torch-autograd-graph-node-metadata" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.graph.Node.metadata">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.metadata" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.metadata" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the metadata.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.autograd.graph.Node.name.html b/main/generated/torch.autograd.graph.Node.name.html
index 8b80493b1fa..7e60c56e00c 100644
--- a/main/generated/torch.autograd.graph.Node.name.html
+++ b/main/generated/torch.autograd.graph.Node.name.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.Node.name<a class="headerlink" href="#torch-autograd-graph-node-name" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.graph.Node.name">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the name.</p>
 <p>Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">torch</span>
diff --git a/main/generated/torch.autograd.graph.Node.next_functions.html b/main/generated/torch.autograd.graph.Node.next_functions.html
index 39b5bc7789a..07ff3ef5394 100644
--- a/main/generated/torch.autograd.graph.Node.next_functions.html
+++ b/main/generated/torch.autograd.graph.Node.next_functions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.autograd.graph.Node.register_hook.html b/main/generated/torch.autograd.graph.Node.register_hook.html
index cfe8d0090ef..b6c7b6bdd59 100644
--- a/main/generated/torch.autograd.graph.Node.register_hook.html
+++ b/main/generated/torch.autograd.graph.Node.register_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.Node.register_hook<a class="headerlink" href="#torch-autograd-graph-node-register-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.graph.Node.register_hook">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">register_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.register_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.register_hook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">register_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.register_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.register_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook.</p>
 <p>The hook will be called every time a gradient with respect to the
 Node is computed. The hook should have the following signature:</p>
diff --git a/main/generated/torch.autograd.graph.Node.register_prehook.html b/main/generated/torch.autograd.graph.Node.register_prehook.html
index 6cb30c55083..cd0aabeff16 100644
--- a/main/generated/torch.autograd.graph.Node.register_prehook.html
+++ b/main/generated/torch.autograd.graph.Node.register_prehook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.Node.register_prehook<a class="headerlink" href="#torch-autograd-graph-node-register-prehook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.graph.Node.register_prehook">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">register_prehook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.register_prehook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.register_prehook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">register_prehook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.register_prehook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.register_prehook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook.</p>
 <p>The hook will be called every time a gradient with respect to the
 Node is computed. The hook should have the following signature:</p>
diff --git a/main/generated/torch.autograd.graph.increment_version.html b/main/generated/torch.autograd.graph.increment_version.html
index e6d0a670136..f6bcd165b87 100644
--- a/main/generated/torch.autograd.graph.increment_version.html
+++ b/main/generated/torch.autograd.graph.increment_version.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.increment_version<a class="headerlink" href="#torch-autograd-graph-increment-version" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.graph.increment_version">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">increment_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#increment_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/graph.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.increment_version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">increment_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#increment_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.increment_version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update autograd metadata tracking whether the given Tensor was modified in place.</p>
 <p>This is to enable more accurate error checking within the autograd engine.
 It is already done automatically by PyTorch functions and within custom Function
diff --git a/main/generated/torch.autograd.profiler.EnforceUnique.html b/main/generated/torch.autograd.profiler.EnforceUnique.html
index 826006a166d..ddc9385fd31 100644
--- a/main/generated/torch.autograd.profiler.EnforceUnique.html
+++ b/main/generated/torch.autograd.profiler.EnforceUnique.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,11 +612,11 @@
 <h1>EnforceUnique<a class="headerlink" href="#enforceunique" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.EnforceUnique">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">EnforceUnique</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#EnforceUnique"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L1020"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.EnforceUnique" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">EnforceUnique</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#EnforceUnique"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1020"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.EnforceUnique" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raises an error if a key is seen more than once.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.EnforceUnique.see">
-<span class="sig-name descname"><span class="pre">see</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#EnforceUnique.see"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L1026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.EnforceUnique.see" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">see</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#EnforceUnique.see"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.EnforceUnique.see" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observe a key and raise an error if it is seen multiple times.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.autograd.profiler.KinetoStepTracker.html b/main/generated/torch.autograd.profiler.KinetoStepTracker.html
index 434ca0ef286..3ce77e8e5d0 100644
--- a/main/generated/torch.autograd.profiler.KinetoStepTracker.html
+++ b/main/generated/torch.autograd.profiler.KinetoStepTracker.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">KinetoStepTracker</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L1102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">KinetoStepTracker</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker" title="Permalink to this definition">¶</a></dt>
 <dd><p>Provides an abstraction for incrementing the step count globally.</p>
 <p>Previously, we only had one place to mark that a step() has occurred
 in the program via pytorch profiler step(). We will now add step hooks
@@ -647,7 +647,7 @@ <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Perm
 for now. The result could be incorrect increments of the step count.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker.current_step">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">current_step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.current_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L1185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.current_step" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">current_step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.current_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.current_step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the latest step for any requester</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -658,7 +658,7 @@ <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker.erase_step_count">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">erase_step_count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.erase_step_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L1154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.erase_step_count" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">erase_step_count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.erase_step_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.erase_step_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove a given requester.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -669,7 +669,7 @@ <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker.increment_step">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">increment_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.increment_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L1161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.increment_step" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">increment_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.increment_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.increment_step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Increments the step count for the requester.</p>
 <p>Additionally if the max over all step counts has incremented then
 trigger the _kineto_step() returns global step count</p>
@@ -682,7 +682,7 @@ <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker.init_step_count">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">init_step_count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.init_step_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.init_step_count" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">init_step_count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.init_step_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.init_step_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize for a given requester.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.autograd.profiler.load_nvprof.html b/main/generated/torch.autograd.profiler.load_nvprof.html
index 4b44cb3ea58..0c6bba9dcfd 100644
--- a/main/generated/torch.autograd.profiler.load_nvprof.html
+++ b/main/generated/torch.autograd.profiler.load_nvprof.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.load_nvprof<a class="headerlink" href="#torch-autograd-profiler-load-nvprof" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.profiler.load_nvprof">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">load_nvprof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#load_nvprof"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.load_nvprof" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">load_nvprof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#load_nvprof"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.load_nvprof" title="Permalink to this definition">¶</a></dt>
 <dd><p>Open an nvprof trace file and parses autograd annotations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.profiler.parse_nvprof_trace.html b/main/generated/torch.autograd.profiler.parse_nvprof_trace.html
index 2fa7034c6fd..74ce2289638 100644
--- a/main/generated/torch.autograd.profiler.parse_nvprof_trace.html
+++ b/main/generated/torch.autograd.profiler.parse_nvprof_trace.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.parse_nvprof_trace<a class="headerlink" href="#torch-autograd-profiler-parse-nvprof-trace" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.profiler.parse_nvprof_trace">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">parse_nvprof_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#parse_nvprof_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L1035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.parse_nvprof_trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">parse_nvprof_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#parse_nvprof_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.parse_nvprof_trace" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.autograd.profiler.profile.export_chrome_trace.html b/main/generated/torch.autograd.profiler.profile.export_chrome_trace.html
index f53da7eb31e..3919ad386f6 100644
--- a/main/generated/torch.autograd.profiler.profile.export_chrome_trace.html
+++ b/main/generated/torch.autograd.profiler.profile.export_chrome_trace.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.profile.export_chrome_trace<a class="headerlink" href="#torch-autograd-profiler-profile-export-chrome-trace" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.profile.export_chrome_trace">
-<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">export_chrome_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.export_chrome_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">export_chrome_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.export_chrome_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Export an EventList as a Chrome tracing tools file.</p>
 <p>The checkpoint can be later loaded and inspected under <code class="docutils literal notranslate"><span class="pre">chrome://tracing</span></code> URL.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.autograd.profiler.profile.key_averages.html b/main/generated/torch.autograd.profiler.profile.key_averages.html
index 83e345b6bb6..cc7e60c3fa3 100644
--- a/main/generated/torch.autograd.profiler.profile.key_averages.html
+++ b/main/generated/torch.autograd.profiler.profile.key_averages.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.profile.key_averages<a class="headerlink" href="#torch-autograd-profiler-profile-key-averages" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.profile.key_averages">
-<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">key_averages</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_by_input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_stack_n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.key_averages"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.key_averages" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">key_averages</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_by_input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_stack_n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.key_averages"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.key_averages" title="Permalink to this definition">¶</a></dt>
 <dd><p>Averages all function events over their keys.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.profiler.profile.self_cpu_time_total.html b/main/generated/torch.autograd.profiler.profile.self_cpu_time_total.html
index 3be390d5961..7e64ed7eb2a 100644
--- a/main/generated/torch.autograd.profiler.profile.self_cpu_time_total.html
+++ b/main/generated/torch.autograd.profiler.profile.self_cpu_time_total.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.autograd.profiler.profile.total_average.html b/main/generated/torch.autograd.profiler.profile.total_average.html
index 7157b6a7a15..5e64c98c806 100644
--- a/main/generated/torch.autograd.profiler.profile.total_average.html
+++ b/main/generated/torch.autograd.profiler.profile.total_average.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.profile.total_average<a class="headerlink" href="#torch-autograd-profiler-profile-total-average" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.profile.total_average">
-<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">total_average</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.total_average"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.total_average" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">total_average</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.total_average"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.total_average" title="Permalink to this definition">¶</a></dt>
 <dd><p>Averages all events.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.autograd.profiler.record_function.html b/main/generated/torch.autograd.profiler.record_function.html
index 19fada87ce3..37b540d19c0 100644
--- a/main/generated/torch.autograd.profiler.record_function.html
+++ b/main/generated/torch.autograd.profiler.record_function.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>record_function<a class="headerlink" href="#record-function" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.record_function">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">record_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#record_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler.py#L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.record_function" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">record_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#record_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.record_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager/function decorator that adds a label to a code block/function when running autograd profiler.
 Label will only appear if CPU activity tracing is enabled.</p>
 <p>It is useful when tracing the code profile.</p>
diff --git a/main/generated/torch.autograd.profiler_util.Interval.html b/main/generated/torch.autograd.profiler_util.Interval.html
index 62495fa53f7..189a2c3ac32 100644
--- a/main/generated/torch.autograd.profiler_util.Interval.html
+++ b/main/generated/torch.autograd.profiler_util.Interval.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,10 +612,10 @@
 <h1>Interval<a class="headerlink" href="#interval" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.Interval">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">Interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#Interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler_util.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.Interval" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">Interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#Interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler_util.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.Interval" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.Interval.elapsed_us">
-<span class="sig-name descname"><span class="pre">elapsed_us</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#Interval.elapsed_us"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler_util.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.Interval.elapsed_us" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">elapsed_us</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#Interval.elapsed_us"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler_util.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.Interval.elapsed_us" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the length of the interval</p>
 </dd></dl>
 
diff --git a/main/generated/torch.autograd.profiler_util.Kernel.html b/main/generated/torch.autograd.profiler_util.Kernel.html
index 13f73df2f8d..bd6391d7098 100644
--- a/main/generated/torch.autograd.profiler_util.Kernel.html
+++ b/main/generated/torch.autograd.profiler_util.Kernel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.autograd.profiler_util.MemRecordsAcc.html b/main/generated/torch.autograd.profiler_util.MemRecordsAcc.html
index 367f19e296a..0fe04d49cb1 100644
--- a/main/generated/torch.autograd.profiler_util.MemRecordsAcc.html
+++ b/main/generated/torch.autograd.profiler_util.MemRecordsAcc.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,11 +612,11 @@
 <h1>MemRecordsAcc<a class="headerlink" href="#memrecordsacc" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.MemRecordsAcc">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">MemRecordsAcc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mem_records</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#MemRecordsAcc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler_util.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.MemRecordsAcc" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">MemRecordsAcc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mem_records</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#MemRecordsAcc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler_util.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.MemRecordsAcc" title="Permalink to this definition">¶</a></dt>
 <dd><p>Acceleration structure for accessing mem_records in interval.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.MemRecordsAcc.in_interval">
-<span class="sig-name descname"><span class="pre">in_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_us</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_us</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#MemRecordsAcc.in_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler_util.py#L743"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.MemRecordsAcc.in_interval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">in_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_us</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_us</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#MemRecordsAcc.in_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler_util.py#L743"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.MemRecordsAcc.in_interval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all records in the given interval
 To maintain backward compatibility, convert us to ns in function</p>
 </dd></dl>
diff --git a/main/generated/torch.autograd.profiler_util.StringTable.html b/main/generated/torch.autograd.profiler_util.StringTable.html
index 71d90c45836..35a45822236 100644
--- a/main/generated/torch.autograd.profiler_util.StringTable.html
+++ b/main/generated/torch.autograd.profiler_util.StringTable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>StringTable<a class="headerlink" href="#stringtable" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.StringTable">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">StringTable</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#StringTable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/profiler_util.py#L723"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.StringTable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">StringTable</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#StringTable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler_util.py#L723"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.StringTable" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.StringTable.clear">
 <span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None.</span>&#160; <span class="pre">Remove</span> <span class="pre">all</span> <span class="pre">items</span> <span class="pre">from</span> <span class="pre">D.</span></span></span><a class="headerlink" href="#torch.autograd.profiler_util.StringTable.clear" title="Permalink to this definition">¶</a></dt>
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@@ -612,7 +612,7 @@
 <h1>torch.block_diag<a class="headerlink" href="#torch-block-diag" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.block_diag">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">block_diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#block_diag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L1401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.block_diag" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">block_diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#block_diag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.block_diag" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a block diagonal matrix from provided tensors.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.bmm.html b/main/generated/torch.bmm.html
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@@ -612,7 +612,7 @@
 <h1>torch.broadcast_shapes<a class="headerlink" href="#torch-broadcast-shapes" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.broadcast_shapes">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">broadcast_shapes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">shapes</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../size.html#torch.Size" title="torch.Size"><span class="pre">Size</span></a></span></span><a class="reference internal" href="../_modules/torch/functional.html#broadcast_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.broadcast_shapes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">broadcast_shapes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">shapes</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../size.html#torch.Size" title="torch.Size"><span class="pre">Size</span></a></span></span><a class="reference internal" href="../_modules/torch/functional.html#broadcast_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.broadcast_shapes" title="Permalink to this definition">¶</a></dt>
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 <p>This is equivalent to
 <code class="docutils literal notranslate"><span class="pre">torch.broadcast_tensors(*map(torch.empty,</span> <span class="pre">shapes))[0].shape</span></code>
diff --git a/main/generated/torch.broadcast_tensors.html b/main/generated/torch.broadcast_tensors.html
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@@ -612,7 +612,7 @@
 <h1>torch.broadcast_tensors<a class="headerlink" href="#torch-broadcast-tensors" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.broadcast_tensors">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">broadcast_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span> <span class="pre">of</span> <span class="pre">Tensors</span></span></span><a class="reference internal" href="../_modules/torch/functional.html#broadcast_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.broadcast_tensors" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">broadcast_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span> <span class="pre">of</span> <span class="pre">Tensors</span></span></span><a class="reference internal" href="../_modules/torch/functional.html#broadcast_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.broadcast_tensors" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcasts the given tensors according to <a class="reference internal" href="../notes/broadcasting.html#broadcasting-semantics"><span class="std std-ref">Broadcasting semantics</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
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diff --git a/main/generated/torch.cartesian_prod.html b/main/generated/torch.cartesian_prod.html
index b5c4fa80aed..023383b73b3 100644
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+++ b/main/generated/torch.cartesian_prod.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>torch.cartesian_prod<a class="headerlink" href="#torch-cartesian-prod" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cartesian_prod">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cartesian_prod</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#cartesian_prod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L1366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cartesian_prod" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cartesian_prod</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#cartesian_prod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cartesian_prod" title="Permalink to this definition">¶</a></dt>
 <dd><p>Do cartesian product of the given sequence of tensors. The behavior is similar to
 python’s <cite>itertools.product</cite>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cat.html b/main/generated/torch.cat.html
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diff --git a/main/generated/torch.cdist.html b/main/generated/torch.cdist.html
index 770c4c9f1ab..db187d8dbda 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cdist<a class="headerlink" href="#torch-cdist" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cdist">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cdist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'use_mm_for_euclid_dist_if_necessary'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#cdist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cdist" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cdist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'use_mm_for_euclid_dist_if_necessary'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#cdist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cdist" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes batched the p-norm distance between each pair of the two collections of row vectors.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ceil.html b/main/generated/torch.ceil.html
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 <h1>torch.chain_matmul<a class="headerlink" href="#torch-chain-matmul" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.chain_matmul">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">chain_matmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">matrices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#chain_matmul"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L1997"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.chain_matmul" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">chain_matmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">matrices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#chain_matmul"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1997"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.chain_matmul" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the matrix product of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi></mrow><annotation encoding="application/x-tex">N</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span></span></span></span></span> 2-D tensors. This product is efficiently computed
 using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
 of arithmetic operations (<a class="reference external" href="https://mitpress.mit.edu/books/introduction-algorithms-third-edition">[CLRS]</a>). Note that since this is a function to compute the product, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi></mrow><annotation encoding="application/x-tex">N</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span></span></span></span></span>
diff --git a/main/generated/torch.cholesky.html b/main/generated/torch.cholesky.html
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index fe7f9d11bb5..70cf28d25b5 100644
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           <div class="pytorch-left-menu-search">
             
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index 1eed4c5e4bc..901bc796408 100644
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index c54281a0af1..910ee2c42b2 100644
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index 5760a6561ed..4611f0f87f8 100644
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index 6d55495e190..fb956dc85f3 100644
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 <h1>torch.compile<a class="headerlink" href="#torch-compile" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compile">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fullgraph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'inductor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../_modules/torch.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L2421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fullgraph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'inductor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../_modules/torch.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L2421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compile" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fullgraph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'inductor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
 <dd><p>Optimizes given model/function using TorchDynamo and specified backend.
diff --git a/main/generated/torch.compiled_with_cxx11_abi.html b/main/generated/torch.compiled_with_cxx11_abi.html
index 11111b90498..aed57b35e2e 100644
--- a/main/generated/torch.compiled_with_cxx11_abi.html
+++ b/main/generated/torch.compiled_with_cxx11_abi.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.compiled_with_cxx11_abi<a class="headerlink" href="#torch-compiled-with-cxx11-abi" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiled_with_cxx11_abi">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compiled_with_cxx11_abi</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#compiled_with_cxx11_abi"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L2215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiled_with_cxx11_abi" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compiled_with_cxx11_abi</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#compiled_with_cxx11_abi"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L2215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiled_with_cxx11_abi" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.compiler.allow_in_graph.html b/main/generated/torch.compiler.allow_in_graph.html
index 3e9e68ea49a..a8b7afe33b9 100644
--- a/main/generated/torch.compiler.allow_in_graph.html
+++ b/main/generated/torch.compiler.allow_in_graph.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.allow_in_graph<a class="headerlink" href="#torch-compiler-allow-in-graph" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.allow_in_graph">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">allow_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#allow_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.allow_in_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">allow_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#allow_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.allow_in_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Tells the compiler frontend (Dynamo) to skip symbolic introspection of the function
 and instead directly write it to the graph when encountered.</p>
 <p>If you are using <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a> (with backend=”inductor” (the default)), or
diff --git a/main/generated/torch.compiler.assume_constant_result.html b/main/generated/torch.compiler.assume_constant_result.html
index 3d52efa01d7..4afffc17fb6 100644
--- a/main/generated/torch.compiler.assume_constant_result.html
+++ b/main/generated/torch.compiler.assume_constant_result.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.assume_constant_result<a class="headerlink" href="#torch-compiler-assume-constant-result" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.assume_constant_result">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">assume_constant_result</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#assume_constant_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.assume_constant_result" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">assume_constant_result</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#assume_constant_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.assume_constant_result" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function is used to mark a function <cite>fn</cite> as having a constant result.
 This allows the compiler to optimize away your function.
 Returns The same function <cite>fn</cite></p>
diff --git a/main/generated/torch.compiler.compile.html b/main/generated/torch.compiler.compile.html
index 55569c4fd88..4150f27b0c7 100644
--- a/main/generated/torch.compiler.compile.html
+++ b/main/generated/torch.compiler.compile.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.compile<a class="headerlink" href="#torch-compiler-compile" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.compile">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.compile" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a> for details on the arguments for this function.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.compiler.cudagraph_mark_step_begin.html b/main/generated/torch.compiler.cudagraph_mark_step_begin.html
index 69951a6df9d..d72ab7650e7 100644
--- a/main/generated/torch.compiler.cudagraph_mark_step_begin.html
+++ b/main/generated/torch.compiler.cudagraph_mark_step_begin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.cudagraph_mark_step_begin<a class="headerlink" href="#torch-compiler-cudagraph-mark-step-begin" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.cudagraph_mark_step_begin">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">cudagraph_mark_step_begin</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#cudagraph_mark_step_begin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.cudagraph_mark_step_begin" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">cudagraph_mark_step_begin</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#cudagraph_mark_step_begin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.cudagraph_mark_step_begin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicates that a new iteration of inference or training is about to begin.</p>
 <p>CUDA Graphs will free tensors of a prior iteration. A new iteration is started on each invocation of
 torch.compile, so long as there is not a pending backward that has not been called.</p>
diff --git a/main/generated/torch.compiler.disable.html b/main/generated/torch.compiler.disable.html
index 4b0bd15bfe6..e3afd50a2b5 100644
--- a/main/generated/torch.compiler.disable.html
+++ b/main/generated/torch.compiler.disable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.disable<a class="headerlink" href="#torch-compiler-disable" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.disable">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">disable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recursive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#disable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.disable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">disable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recursive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#disable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.disable" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function provides a decorator to disable compilation on a function.
 It also provides the option of recursively disabling called functions.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.compiler.is_compiling.html b/main/generated/torch.compiler.is_compiling.html
index 82c3c310007..e875387e475 100644
--- a/main/generated/torch.compiler.is_compiling.html
+++ b/main/generated/torch.compiler.is_compiling.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.is_compiling<a class="headerlink" href="#torch-compiler-is-compiling" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.is_compiling">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_compiling</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_compiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_compiling" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_compiling</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_compiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_compiling" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicates whether a graph is executed/traced as part of torch.compile() or torch.export().</p>
 <dl class="simple">
 <dt>Note that there are 2 other related flags that should deprecated eventually:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.compiler.is_dynamo_compiling.html b/main/generated/torch.compiler.is_dynamo_compiling.html
index 446b8ef48e2..bdc4c9f6698 100644
--- a/main/generated/torch.compiler.is_dynamo_compiling.html
+++ b/main/generated/torch.compiler.is_dynamo_compiling.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.is_dynamo_compiling<a class="headerlink" href="#torch-compiler-is-dynamo-compiling" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.is_dynamo_compiling">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_dynamo_compiling</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_dynamo_compiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_dynamo_compiling" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_dynamo_compiling</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_dynamo_compiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_dynamo_compiling" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicates whether a graph is traced via TorchDynamo.</p>
 <p>It’s stricter than is_compiling() flag, as it would only be set to True when
 TorchDynamo is used.</p>
diff --git a/main/generated/torch.compiler.is_exporting.html b/main/generated/torch.compiler.is_exporting.html
index 1efe85d6c62..b762cd04af9 100644
--- a/main/generated/torch.compiler.is_exporting.html
+++ b/main/generated/torch.compiler.is_exporting.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.is_exporting<a class="headerlink" href="#torch-compiler-is-exporting" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.is_exporting">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_exporting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_exporting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L417"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_exporting" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_exporting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_exporting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L417"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_exporting" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicated whether we’re under exporting.</p>
 <p>It’s stricter than is_compiling() flag, as it would only be set to True when
 torch.export is used.</p>
diff --git a/main/generated/torch.compiler.list_backends.html b/main/generated/torch.compiler.list_backends.html
index d819227dd4b..4317f17e80c 100644
--- a/main/generated/torch.compiler.list_backends.html
+++ b/main/generated/torch.compiler.list_backends.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.list_backends<a class="headerlink" href="#torch-compiler-list-backends" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.list_backends">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">list_backends</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exclude_tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('debug',</span> <span class="pre">'experimental')</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#list_backends"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.list_backends" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">list_backends</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exclude_tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('debug',</span> <span class="pre">'experimental')</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#list_backends"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.list_backends" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return valid strings that can be passed to <cite>torch.compile(…, backend=”name”)</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.compiler.reset.html b/main/generated/torch.compiler.reset.html
index 88c738b42d5..b4454ebc17c 100644
--- a/main/generated/torch.compiler.reset.html
+++ b/main/generated/torch.compiler.reset.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.reset<a class="headerlink" href="#torch-compiler-reset" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.reset">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function clears all compilation caches and restores the system to its initial state.
 It is recommended to call this function, especially after using operations like <cite>torch.compile(…)</cite>
 to ensure a clean state before another unrelated compilation</p>
diff --git a/main/generated/torch.compiler.set_stance.html b/main/generated/torch.compiler.set_stance.html
index 21cb3a69c14..f2a2861500b 100644
--- a/main/generated/torch.compiler.set_stance.html
+++ b/main/generated/torch.compiler.set_stance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.set_stance<a class="headerlink" href="#torch-compiler-set-stance" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.set_stance">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">set_stance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_guard_eval_unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#set_stance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.set_stance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">set_stance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_guard_eval_unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#set_stance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.set_stance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current stance of the compiler.
 Can be used as a function, context manager, or decorator.
 Do not use this function inside a <cite>torch.compile</cite> region - an error will be raised otherwise.</p>
diff --git a/main/generated/torch.compiler.substitute_in_graph.html b/main/generated/torch.compiler.substitute_in_graph.html
index 48fe756bf1c..093d225a6c3 100644
--- a/main/generated/torch.compiler.substitute_in_graph.html
+++ b/main/generated/torch.compiler.substitute_in_graph.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.substitute_in_graph<a class="headerlink" href="#torch-compiler-substitute-in-graph" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.substitute_in_graph">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">substitute_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">can_constant_fold_through</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_signature_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#substitute_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/compiler/__init__.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.substitute_in_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">substitute_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">can_constant_fold_through</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_signature_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#substitute_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.substitute_in_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a polyfill handler for a function, usually a C function from the C extension, to be
 used in place of the original function when inlining the original function in the graph.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.complex.html b/main/generated/torch.complex.html
index 5a4e704e85e..63294197d15 100644
--- a/main/generated/torch.complex.html
+++ b/main/generated/torch.complex.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.concat.html b/main/generated/torch.concat.html
index 996aad92ea4..d7fc41c4255 100644
--- a/main/generated/torch.concat.html
+++ b/main/generated/torch.concat.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.concatenate.html b/main/generated/torch.concatenate.html
index 0ad05d989c8..5c3c1a83c08 100644
--- a/main/generated/torch.concatenate.html
+++ b/main/generated/torch.concatenate.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cond.html b/main/generated/torch.cond.html
index 0d3202c2198..4a143b938eb 100644
--- a/main/generated/torch.cond.html
+++ b/main/generated/torch.cond.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cond<a class="headerlink" href="#torch-cond" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cond">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_higher_order_ops/cond.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cond" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_higher_order_ops/cond.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cond" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conditionally applies <cite>true_fn</cite> or <cite>false_fn</cite>.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.conj.html b/main/generated/torch.conj.html
index 7f404b6e4da..bf4be62a18c 100644
--- a/main/generated/torch.conj.html
+++ b/main/generated/torch.conj.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.conj_physical.html b/main/generated/torch.conj_physical.html
index 164335abaf9..e246852588b 100644
--- a/main/generated/torch.conj_physical.html
+++ b/main/generated/torch.conj_physical.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.copysign.html b/main/generated/torch.copysign.html
index 7370d954c0c..915acb6cff6 100644
--- a/main/generated/torch.copysign.html
+++ b/main/generated/torch.copysign.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.corrcoef.html b/main/generated/torch.corrcoef.html
index 4fa034876d1..4a4bb99ecff 100644
--- a/main/generated/torch.corrcoef.html
+++ b/main/generated/torch.corrcoef.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cos.html b/main/generated/torch.cos.html
index 1214229cb03..c10a16f087a 100644
--- a/main/generated/torch.cos.html
+++ b/main/generated/torch.cos.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cosh.html b/main/generated/torch.cosh.html
index d12a85e307d..f527e584060 100644
--- a/main/generated/torch.cosh.html
+++ b/main/generated/torch.cosh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.count_nonzero.html b/main/generated/torch.count_nonzero.html
index dc6bdbb052c..0045f3f20c2 100644
--- a/main/generated/torch.count_nonzero.html
+++ b/main/generated/torch.count_nonzero.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cov.html b/main/generated/torch.cov.html
index 4ac713dec10..086cc38a832 100644
--- a/main/generated/torch.cov.html
+++ b/main/generated/torch.cov.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cpu.Stream.html b/main/generated/torch.cpu.Stream.html
index 0fdc20cb292..b0cb35144b0 100644
--- a/main/generated/torch.cpu.Stream.html
+++ b/main/generated/torch.cpu.Stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cpu.Stream">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.Stream" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.Stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>N.B. This class only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.cpu.StreamContext.html b/main/generated/torch.cpu.StreamContext.html
index 7f4f8c351bf..e621c80fcdc 100644
--- a/main/generated/torch.cpu.StreamContext.html
+++ b/main/generated/torch.cpu.StreamContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StreamContext<a class="headerlink" href="#streamcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cpu.StreamContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/__init__.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.StreamContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.StreamContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that selects a given stream.</p>
 <p>N.B. This class only exists to facilitate device-agnostic code</p>
 </dd></dl>
diff --git a/main/generated/torch.cpu.current_device.html b/main/generated/torch.cpu.current_device.html
index 302ed07874d..7e64be5f97e 100644
--- a/main/generated/torch.cpu.current_device.html
+++ b/main/generated/torch.cpu.current_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.current_device<a class="headerlink" href="#torch-cpu-current-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.current_device">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/__init__.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.current_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.current_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns current device for cpu. Always ‘cpu’.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cpu.current_stream.html b/main/generated/torch.cpu.current_stream.html
index 7936ca9b019..6f280e809af 100644
--- a/main/generated/torch.cpu.current_stream.html
+++ b/main/generated/torch.cpu.current_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.current_stream<a class="headerlink" href="#torch-cpu-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/__init__.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the currently selected <a class="reference internal" href="torch.cpu.Stream.html#torch.cpu.Stream" title="torch.cpu.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cpu.device_count.html b/main/generated/torch.cpu.device_count.html
index 6b2133ce385..fe587417c87 100644
--- a/main/generated/torch.cpu.device_count.html
+++ b/main/generated/torch.cpu.device_count.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.device_count<a class="headerlink" href="#torch-cpu-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/__init__.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns number of CPU devices (not cores). Always 1.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cpu.is_available.html b/main/generated/torch.cpu.is_available.html
index dbaebafe8f5..f98155366b0 100644
--- a/main/generated/torch.cpu.is_available.html
+++ b/main/generated/torch.cpu.is_available.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.is_available<a class="headerlink" href="#torch-cpu-is-available" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/__init__.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a bool indicating if CPU is currently available.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cpu.set_device.html b/main/generated/torch.cpu.set_device.html
index 662d32131f5..7768082e0bf 100644
--- a/main/generated/torch.cpu.set_device.html
+++ b/main/generated/torch.cpu.set_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.set_device<a class="headerlink" href="#torch-cpu-set-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.set_device">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/__init__.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.set_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.set_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the current device, in CPU we do nothing.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cpu.stream.html b/main/generated/torch.cpu.stream.html
index 232477f02b5..b8c9b0b60e2 100644
--- a/main/generated/torch.cpu.stream.html
+++ b/main/generated/torch.cpu.stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.stream<a class="headerlink" href="#torch-cpu-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/__init__.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around the Context-manager StreamContext that
 selects a given stream.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
diff --git a/main/generated/torch.cpu.synchronize.html b/main/generated/torch.cpu.synchronize.html
index 7e4c19f4cd4..3fdee87c9d0 100644
--- a/main/generated/torch.cpu.synchronize.html
+++ b/main/generated/torch.cpu.synchronize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.synchronize<a class="headerlink" href="#torch-cpu-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cpu/__init__.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits for all kernels in all streams on the CPU device to complete.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cross.html b/main/generated/torch.cross.html
index 6711c43d2b9..e7d9521cd50 100644
--- a/main/generated/torch.cross.html
+++ b/main/generated/torch.cross.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cuda.CUDAGraph.html b/main/generated/torch.cuda.CUDAGraph.html
index 9c562db1e0d..74310bea819 100644
--- a/main/generated/torch.cuda.CUDAGraph.html
+++ b/main/generated/torch.cuda.CUDAGraph.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">CUDAGraph</span></span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">CUDAGraph</span></span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a CUDA graph.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -620,7 +620,7 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.capture_begin">
-<span class="sig-name descname"><span class="pre">capture_begin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capture_error_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'global'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.capture_begin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.capture_begin" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">capture_begin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capture_error_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'global'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.capture_begin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.capture_begin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Begin capturing CUDA work on the current stream.</p>
 <p>Typically, you shouldn’t call <code class="docutils literal notranslate"><span class="pre">capture_begin</span></code> yourself.
 Use <a class="reference internal" href="torch.cuda.graph.html#torch.cuda.graph" title="torch.cuda.graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">graph</span></code></a> or <a class="reference internal" href="torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables" title="torch.cuda.make_graphed_callables"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_graphed_callables()</span></code></a>,
@@ -643,7 +643,7 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.capture_end">
-<span class="sig-name descname"><span class="pre">capture_end</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.capture_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.capture_end" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">capture_end</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.capture_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.capture_end" title="Permalink to this definition">¶</a></dt>
 <dd><p>End CUDA graph capture on the current stream.</p>
 <p>After <code class="docutils literal notranslate"><span class="pre">capture_end</span></code>, <code class="docutils literal notranslate"><span class="pre">replay</span></code> may be called on this instance.</p>
 <p>Typically, you shouldn’t call <code class="docutils literal notranslate"><span class="pre">capture_end</span></code> yourself.
@@ -653,7 +653,7 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.debug_dump">
-<span class="sig-name descname"><span class="pre">debug_dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.debug_dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.debug_dump" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">debug_dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.debug_dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.debug_dump" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>debug_path</strong> (<em>required</em>) – Path to dump the graph to.</p>
@@ -665,13 +665,13 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.enable_debug_mode">
-<span class="sig-name descname"><span class="pre">enable_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.enable_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.enable_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enable_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.enable_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.enable_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable debugging mode for CUDAGraph.debug_dump.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.pool">
-<span class="sig-name descname"><span class="pre">pool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.pool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.pool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an opaque token representing the id of this graph’s memory pool.</p>
 <p>This id can optionally be passed to another graph’s <code class="docutils literal notranslate"><span class="pre">capture_begin</span></code>,
 which hints the other graph may share the same memory pool.</p>
@@ -679,13 +679,13 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.replay">
-<span class="sig-name descname"><span class="pre">replay</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.replay"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.replay" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replay</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.replay"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.replay" title="Permalink to this definition">¶</a></dt>
 <dd><p>Replay the CUDA work captured by this graph.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.reset">
-<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Delete the graph currently held by this instance.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.cuda.CUDAPluggableAllocator.html b/main/generated/torch.cuda.CUDAPluggableAllocator.html
index 544d568b39a..9f76632bf6f 100644
--- a/main/generated/torch.cuda.CUDAPluggableAllocator.html
+++ b/main/generated/torch.cuda.CUDAPluggableAllocator.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CUDAPluggableAllocator<a class="headerlink" href="#cudapluggableallocator" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.CUDAPluggableAllocator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">CUDAPluggableAllocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_to_so_file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alloc_fn_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">free_fn_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#CUDAPluggableAllocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAPluggableAllocator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">CUDAPluggableAllocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_to_so_file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alloc_fn_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">free_fn_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#CUDAPluggableAllocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAPluggableAllocator" title="Permalink to this definition">¶</a></dt>
 <dd><p>CUDA memory allocator loaded from a so file.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.cuda.Event.html b/main/generated/torch.cuda.Event.html
index 9a3d9c467b4..0807a51ef46 100644
--- a/main/generated/torch.cuda.Event.html
+++ b/main/generated/torch.cuda.Event.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.Event">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interprocess</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interprocess</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a CUDA event.</p>
 <p>CUDA events are synchronization markers that can be used to monitor the
 device’s progress, to accurately measure timing, and to synchronize CUDA
@@ -634,7 +634,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.elapsed_time">
-<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the time elapsed.</p>
 <p>Time reported in milliseconds after the event was recorded and
 before the end_event was recorded.</p>
@@ -642,20 +642,20 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.from_ipc_handle">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_ipc_handle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.from_ipc_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.from_ipc_handle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_ipc_handle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.from_ipc_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.from_ipc_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reconstruct an event from an IPC handle on the given device.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.ipc_handle">
-<span class="sig-name descname"><span class="pre">ipc_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.ipc_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.ipc_handle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ipc_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.ipc_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.ipc_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an IPC handle of this event.</p>
 <p>If not recorded yet, the event will use the current device.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all work currently captured by event has completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -667,7 +667,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.record">
-<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.record" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record the event in a given stream.</p>
 <p>Uses <code class="docutils literal notranslate"><span class="pre">torch.cuda.current_stream()</span></code> if no stream is specified. The
 stream’s device must match the event’s device.</p>
@@ -675,7 +675,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for the event to complete.</p>
 <p>Waits until the completion of all work currently captured in this event.
 This prevents the CPU thread from proceeding until the event completes.</p>
@@ -692,7 +692,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the given stream wait for this event.</p>
 <p>Use <code class="docutils literal notranslate"><span class="pre">torch.cuda.current_stream()</span></code> if no stream is specified.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.cuda.ExternalStream.html b/main/generated/torch.cuda.ExternalStream.html
index 4918a047bee..25ff224853f 100644
--- a/main/generated/torch.cuda.ExternalStream.html
+++ b/main/generated/torch.cuda.ExternalStream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">ExternalStream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#ExternalStream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">ExternalStream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#ExternalStream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around an externally allocated CUDA stream.</p>
 <p>This class is used to wrap streams allocated in other libraries in order
 to facilitate data exchange and multi-library interactions.</p>
@@ -635,7 +635,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all the work submitted has been completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -649,7 +649,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.record_event">
-<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.record_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.record_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -664,7 +664,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all the kernels in this stream to complete.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -677,7 +677,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.wait_event">
-<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.wait_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.wait_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the stream wait for an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -695,7 +695,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.wait_stream">
-<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.wait_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.wait_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize with another stream.</p>
 <p>All future work submitted to this stream will wait until all kernels
 submitted to a given stream at the time of call complete.</p>
diff --git a/main/generated/torch.cuda.MemPool.html b/main/generated/torch.cuda.MemPool.html
index 2d99c14b4d8..2cfc318d01a 100644
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+++ b/main/generated/torch.cuda.MemPool.html
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@@ -612,7 +612,7 @@
 <h1>MemPool<a class="headerlink" href="#mempool" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.MemPool">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">MemPool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L1021"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">MemPool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1021"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool" title="Permalink to this definition">¶</a></dt>
 <dd><p>MemPool represents a pool of memory in a caching allocator. Currently,
 it’s just the ID of the pool object maintained in the CUDACachingAllocator.</p>
 <dl class="field-list simple">
@@ -638,7 +638,7 @@ <h1>MemPool<a class="headerlink" href="#mempool" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.MemPool.snapshot">
-<span class="sig-name descname"><span class="pre">snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool.snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L1051"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool.snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool.snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1051"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool.snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a snapshot of the CUDA memory allocator pool state across all
 devices.</p>
 <p>Interpreting the output of this function requires familiarity with the
@@ -652,7 +652,7 @@ <h1>MemPool<a class="headerlink" href="#mempool" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.MemPool.use_count">
-<span class="sig-name descname"><span class="pre">use_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool.use_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool.use_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">use_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool.use_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool.use_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the reference count of this pool.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.MemPoolContext.html b/main/generated/torch.cuda.MemPoolContext.html
index b3986f1421b..7a6f945b2ef 100644
--- a/main/generated/torch.cuda.MemPoolContext.html
+++ b/main/generated/torch.cuda.MemPoolContext.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>MemPoolContext<a class="headerlink" href="#mempoolcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.MemPoolContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">MemPoolContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPoolContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L1002"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPoolContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">MemPoolContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPoolContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1002"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPoolContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>MemPoolContext holds the currently active pool and stashes the previous
 pool. On deletion it makes the previous pool active.</p>
 <dl class="field-list simple">
@@ -625,7 +625,7 @@ <h1>MemPoolContext<a class="headerlink" href="#mempoolcontext" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.MemPoolContext.active_pool">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">active_pool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPoolContext.active_pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L1015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPoolContext.active_pool" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">active_pool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPoolContext.active_pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPoolContext.active_pool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the active MemPool</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.OutOfMemoryError.html b/main/generated/torch.cuda.OutOfMemoryError.html
index 1081e753f24..4d28a34b045 100644
--- a/main/generated/torch.cuda.OutOfMemoryError.html
+++ b/main/generated/torch.cuda.OutOfMemoryError.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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diff --git a/main/generated/torch.cuda.Stream.html b/main/generated/torch.cuda.Stream.html
index 1131154382c..0680befcd6a 100644
--- a/main/generated/torch.cuda.Stream.html
+++ b/main/generated/torch.cuda.Stream.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.Stream">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a CUDA stream.</p>
 <p>A CUDA stream is a linear sequence of execution that belongs to a specific
 device, independent from other streams. It supports with statement as a
@@ -634,7 +634,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all the work submitted has been completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -648,7 +648,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.record_event">
-<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.record_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.record_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.record_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.record_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -663,7 +663,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all the kernels in this stream to complete.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -676,7 +676,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.wait_event">
-<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the stream wait for an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -694,7 +694,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.wait_stream">
-<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/streams.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize with another stream.</p>
 <p>All future work submitted to this stream will wait until all kernels
 submitted to a given stream at the time of call complete.</p>
diff --git a/main/generated/torch.cuda.StreamContext.html b/main/generated/torch.cuda.StreamContext.html
index 91437bfd6f8..95a9a3b7a7c 100644
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@@ -612,7 +612,7 @@
 <h1>StreamContext<a class="headerlink" href="#streamcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.StreamContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.StreamContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.StreamContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that selects a given stream.</p>
 <p>All CUDA kernels queued within its context will be enqueued on a selected
 stream.</p>
diff --git a/main/generated/torch.cuda.caching_allocator_alloc.html b/main/generated/torch.cuda.caching_allocator_alloc.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.caching_allocator_alloc<a class="headerlink" href="#torch-cuda-caching-allocator-alloc" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.caching_allocator_alloc">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_alloc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_alloc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.caching_allocator_alloc" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_alloc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_alloc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.caching_allocator_alloc" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a memory allocation using the CUDA memory allocator.</p>
 <p>Memory is allocated for a given device and a stream, this
 function is intended to be used for interoperability with other
diff --git a/main/generated/torch.cuda.caching_allocator_delete.html b/main/generated/torch.cuda.caching_allocator_delete.html
index ee7a5f1c35d..af798106614 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.caching_allocator_delete<a class="headerlink" href="#torch-cuda-caching-allocator-delete" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.caching_allocator_delete">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_delete</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mem_ptr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_delete"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.caching_allocator_delete" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_delete</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mem_ptr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_delete"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.caching_allocator_delete" title="Permalink to this definition">¶</a></dt>
 <dd><p>Delete memory allocated using the CUDA memory allocator.</p>
 <p>Memory allocated with <a class="reference internal" href="torch.cuda.caching_allocator_alloc.html#torch.cuda.caching_allocator_alloc" title="torch.cuda.caching_allocator_alloc"><code class="xref py py-func docutils literal notranslate"><span class="pre">caching_allocator_alloc()</span></code></a>.
 is freed here. The associated device and stream are tracked inside
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index 1d45d63e8c2..4c88b36488c 100644
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+++ b/main/generated/torch.cuda.can_device_access_peer.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.can_device_access_peer<a class="headerlink" href="#torch-cuda-can-device-access-peer" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.can_device_access_peer">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">can_device_access_peer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peer_device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#can_device_access_peer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.can_device_access_peer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">can_device_access_peer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peer_device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#can_device_access_peer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.can_device_access_peer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if peer access between two devices is possible.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.change_current_allocator.html b/main/generated/torch.cuda.change_current_allocator.html
index d6649dccb0e..56a1abd9e45 100644
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+++ b/main/generated/torch.cuda.change_current_allocator.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.change_current_allocator<a class="headerlink" href="#torch-cuda-change-current-allocator" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.change_current_allocator">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">change_current_allocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">allocator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#change_current_allocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L979"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.change_current_allocator" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">change_current_allocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">allocator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#change_current_allocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L979"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.change_current_allocator" title="Permalink to this definition">¶</a></dt>
 <dd><p>Change the currently used memory allocator to be the one provided.</p>
 <p>If the current allocator has already been used/initialized, this function will error.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cuda.clock_rate.html b/main/generated/torch.cuda.clock_rate.html
index b6fa51edaee..a6087be81ed 100644
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+++ b/main/generated/torch.cuda.clock_rate.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.clock_rate<a class="headerlink" href="#torch-cuda-clock-rate" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.clock_rate">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">clock_rate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#clock_rate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.clock_rate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">clock_rate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#clock_rate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.clock_rate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the clock speed of the GPU SM in Hz Hertz over the past sample period as given by <cite>nvidia-smi</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.comm.broadcast.html b/main/generated/torch.cuda.comm.broadcast.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.broadcast<a class="headerlink" href="#torch-cuda-comm-broadcast" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.comm.broadcast">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#broadcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/comm.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.broadcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#broadcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.broadcast" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcasts a tensor to specified GPU devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.comm.broadcast_coalesced.html b/main/generated/torch.cuda.comm.broadcast_coalesced.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.broadcast_coalesced<a class="headerlink" href="#torch-cuda-comm-broadcast-coalesced" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.comm.broadcast_coalesced">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">broadcast_coalesced</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10485760</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#broadcast_coalesced"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/comm.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.broadcast_coalesced" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">broadcast_coalesced</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10485760</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#broadcast_coalesced"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.broadcast_coalesced" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcast a sequence of tensors to the specified GPUs.</p>
 <p>Small tensors are first coalesced into a buffer to reduce the number of synchronizations.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cuda.comm.gather.html b/main/generated/torch.cuda.comm.gather.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.gather<a class="headerlink" href="#torch-cuda-comm-gather" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.comm.gather">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/comm.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.gather" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.gather" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers tensors from multiple GPU devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.comm.reduce_add.html b/main/generated/torch.cuda.comm.reduce_add.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.reduce_add<a class="headerlink" href="#torch-cuda-comm-reduce-add" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">reduce_add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#reduce_add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/comm.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.reduce_add" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">reduce_add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#reduce_add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.reduce_add" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sum tensors from multiple GPUs.</p>
 <p>All inputs should have matching shapes, dtype, and layout. The output tensor
 will be of the same shape, dtype, and layout.</p>
diff --git a/main/generated/torch.cuda.comm.scatter.html b/main/generated/torch.cuda.comm.scatter.html
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+++ b/main/generated/torch.cuda.comm.scatter.html
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.scatter<a class="headerlink" href="#torch-cuda-comm-scatter" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">streams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/comm.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.scatter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">streams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.scatter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatters tensor across multiple GPUs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.cudart.html b/main/generated/torch.cuda.cudart.html
index fbf826d9bb5..0a6cf5580f0 100644
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+++ b/main/generated/torch.cuda.cudart.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.cudart<a class="headerlink" href="#torch-cuda-cudart" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.cudart">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">cudart</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#cudart"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.cudart" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">cudart</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#cudart"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.cudart" title="Permalink to this definition">¶</a></dt>
 <dd><p>Retrieves the CUDA runtime API module.</p>
 <p>This function initializes the CUDA runtime environment if it is not already
 initialized and returns the CUDA runtime API module (_cudart). The CUDA
diff --git a/main/generated/torch.cuda.current_blas_handle.html b/main/generated/torch.cuda.current_blas_handle.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.current_blas_handle<a class="headerlink" href="#torch-cuda-current-blas-handle" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_blas_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_blas_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1079"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_blas_handle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_blas_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_blas_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1079"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_blas_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return cublasHandle_t pointer to current cuBLAS handle</p>
 </dd></dl>
 
diff --git a/main/generated/torch.cuda.current_device.html b/main/generated/torch.cuda.current_device.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.current_device<a class="headerlink" href="#torch-cuda-current-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.current_device">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.current_stream.html b/main/generated/torch.cuda.current_stream.html
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+++ b/main/generated/torch.cuda.current_stream.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.current_stream<a class="headerlink" href="#torch-cuda-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the currently selected <a class="reference internal" href="torch.cuda.Stream.html#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.default_stream.html b/main/generated/torch.cuda.default_stream.html
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+++ b/main/generated/torch.cuda.default_stream.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.default_stream<a class="headerlink" href="#torch-cuda-default-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.default_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">default_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#default_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.default_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">default_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#default_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.default_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the default <a class="reference internal" href="torch.cuda.Stream.html#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.device.html b/main/generated/torch.cuda.device.html
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@@ -612,7 +612,7 @@
 <h1>device<a class="headerlink" href="#device" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.device">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.device_count.html b/main/generated/torch.cuda.device_count.html
index cf544325a52..5eb585ccad4 100644
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+++ b/main/generated/torch.cuda.device_count.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.device_count<a class="headerlink" href="#torch-cuda-device-count" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of GPUs available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.device_memory_used.html b/main/generated/torch.cuda.device_memory_used.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.device_memory_used<a class="headerlink" href="#torch-cuda-device-memory-used" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.device_memory_used">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_memory_used</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_memory_used"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_memory_used" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_memory_used</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_memory_used"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_memory_used" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return used global (device) memory in bytes as given by <cite>nvidia-smi</cite> or <cite>amd-smi</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.device_of.html b/main/generated/torch.cuda.device_of.html
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+++ b/main/generated/torch.cuda.device_of.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>device_of<a class="headerlink" href="#device-of" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.device_of">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L462"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_of" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L462"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_of" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the current device to that of given object.</p>
 <p>You can use both tensors and storages as arguments. If a given object is
 not allocated on a GPU, this is a no-op.</p>
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+++ b/main/generated/torch.cuda.empty_cache.html
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.cuda.empty_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.empty_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.empty_cache" title="Permalink to this definition">¶</a></dt>
 <dd><p>Release all unoccupied cached memory currently held by the caching
 allocator so that those can be used in other GPU application and visible in
 <cite>nvidia-smi</cite>.</p>
diff --git a/main/generated/torch.cuda.get_allocator_backend.html b/main/generated/torch.cuda.get_allocator_backend.html
index 8f98ef51bf7..e44774adc64 100644
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+++ b/main/generated/torch.cuda.get_allocator_backend.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_allocator_backend<a class="headerlink" href="#torch-cuda-get-allocator-backend" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_allocator_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_allocator_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#get_allocator_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_allocator_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_allocator_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#get_allocator_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_allocator_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a string describing the active allocator backend as set by
 <code class="docutils literal notranslate"><span class="pre">PYTORCH_CUDA_ALLOC_CONF</span></code>. Currently available backends are
 <code class="docutils literal notranslate"><span class="pre">native</span></code> (PyTorch’s native caching allocator) and <cite>cudaMallocAsync`</cite>
diff --git a/main/generated/torch.cuda.get_arch_list.html b/main/generated/torch.cuda.get_arch_list.html
index 9ab78ed47cf..4dfcd50235c 100644
--- a/main/generated/torch.cuda.get_arch_list.html
+++ b/main/generated/torch.cuda.get_arch_list.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_arch_list<a class="headerlink" href="#torch-cuda-get-arch-list" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_arch_list">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_arch_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_arch_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_arch_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_arch_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_arch_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_arch_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return list CUDA architectures this library was compiled for.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.get_device_capability.html b/main/generated/torch.cuda.get_device_capability.html
index 97f94375b49..1400f6a307c 100644
--- a/main/generated/torch.cuda.get_device_capability.html
+++ b/main/generated/torch.cuda.get_device_capability.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_device_capability<a class="headerlink" href="#torch-cuda-get-device-capability" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_device_capability">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L507"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_capability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L507"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_capability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the cuda capability of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_device_name.html b/main/generated/torch.cuda.get_device_name.html
index c9696cd4cfb..3086d52a3fa 100644
--- a/main/generated/torch.cuda.get_device_name.html
+++ b/main/generated/torch.cuda.get_device_name.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_device_name<a class="headerlink" href="#torch-cuda-get-device-name" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_device_name">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L492"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L492"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the name of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_device_properties.html b/main/generated/torch.cuda.get_device_properties.html
index 718dc7c355e..9a919f0b8c5 100644
--- a/main/generated/torch.cuda.get_device_properties.html
+++ b/main/generated/torch.cuda.get_device_properties.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_device_properties<a class="headerlink" href="#torch-cuda-get-device-properties" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_device_properties">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_properties</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_properties"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_properties" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_properties</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_properties"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_properties" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the properties of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_gencode_flags.html b/main/generated/torch.cuda.get_gencode_flags.html
index 40edb496ecd..fd88707824f 100644
--- a/main/generated/torch.cuda.get_gencode_flags.html
+++ b/main/generated/torch.cuda.get_gencode_flags.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_gencode_flags<a class="headerlink" href="#torch-cuda-get-gencode-flags" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_gencode_flags">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_gencode_flags</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_gencode_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_gencode_flags" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_gencode_flags</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_gencode_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_gencode_flags" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return NVCC gencode flags this library was compiled with.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.get_per_process_memory_fraction.html b/main/generated/torch.cuda.get_per_process_memory_fraction.html
index e8d1b8bfa35..978b7164745 100644
--- a/main/generated/torch.cuda.get_per_process_memory_fraction.html
+++ b/main/generated/torch.cuda.get_per_process_memory_fraction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_per_process_memory_fraction<a class="headerlink" href="#torch-cuda-get-per-process-memory-fraction" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_per_process_memory_fraction">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#get_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#get_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get memory fraction for a process.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_rng_state.html b/main/generated/torch.cuda.get_rng_state.html
index 0cc3211c136..0fedb2921af 100644
--- a/main/generated/torch.cuda.get_rng_state.html
+++ b/main/generated/torch.cuda.get_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_rng_state<a class="headerlink" href="#torch-cuda-get-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/random.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the random number generator state of the specified GPU as a ByteTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_rng_state_all.html b/main/generated/torch.cuda.get_rng_state_all.html
index 4f253798049..e58e73121be 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_rng_state_all<a class="headerlink" href="#torch-cuda-get-rng-state-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_rng_state_all">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_rng_state_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#get_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/random.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_rng_state_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_rng_state_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#get_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_rng_state_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of ByteTensor representing the random number states of all devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.get_stream_from_external.html b/main/generated/torch.cuda.get_stream_from_external.html
index d1e2d1f071f..113a88d2d07 100644
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+++ b/main/generated/torch.cuda.get_stream_from_external.html
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     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_stream_from_external<a class="headerlink" href="#torch-cuda-get-stream-from-external" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_stream_from_external">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_stream_from_external</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_stream_from_external"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1051"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_stream_from_external" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_stream_from_external</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_stream_from_external"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1051"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_stream_from_external" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a <a class="reference internal" href="torch.cuda.Stream.html#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> from an externally allocated CUDA stream.</p>
 <p>This function is used to wrap streams allocated in other libraries in order
 to facilitate data exchange and multi-library interactions.</p>
diff --git a/main/generated/torch.cuda.get_sync_debug_mode.html b/main/generated/torch.cuda.get_sync_debug_mode.html
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_sync_debug_mode<a class="headerlink" href="#torch-cuda-get-sync-debug-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_sync_debug_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_sync_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_sync_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_sync_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_sync_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_sync_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_sync_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return current value of debug mode for cuda synchronizing operations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.graph.html b/main/generated/torch.cuda.graph.html
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>graph<a class="headerlink" href="#graph" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.graph">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cuda_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capture_error_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'global'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.graph" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cuda_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capture_error_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'global'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that captures CUDA work into a <a class="reference internal" href="torch.cuda.CUDAGraph.html#torch.cuda.CUDAGraph" title="torch.cuda.CUDAGraph"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.CUDAGraph</span></code></a> object for later replay.</p>
 <p>See <a class="reference internal" href="../notes/cuda.html#cuda-graph-semantics"><span class="std std-ref">CUDA Graphs</span></a> for a general introduction,
 detailed use, and constraints.</p>
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.graph_pool_handle<a class="headerlink" href="#torch-cuda-graph-pool-handle" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.graph_pool_handle">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">graph_pool_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#graph_pool_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.graph_pool_handle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">graph_pool_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#graph_pool_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.graph_pool_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an opaque token representing the id of a graph memory pool.</p>
 <p>See <a class="reference internal" href="../notes/cuda.html#graph-memory-management"><span class="std std-ref">Graph memory management</span></a>.</p>
 <div class="admonition warning">
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index 5225a4489c7..47bc36429dd 100644
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@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.init<a class="headerlink" href="#torch-cuda-init" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.init">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.init" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.init" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize PyTorch’s CUDA state.</p>
 <p>You may need to call this explicitly if you are interacting with
 PyTorch via its C API, as Python bindings for CUDA functionality
diff --git a/main/generated/torch.cuda.initial_seed.html b/main/generated/torch.cuda.initial_seed.html
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+++ b/main/generated/torch.cuda.initial_seed.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.initial_seed<a class="headerlink" href="#torch-cuda-initial-seed" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.initial_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/random.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.initial_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.initial_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current random seed of the current GPU.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.cuda.ipc_collect.html b/main/generated/torch.cuda.ipc_collect.html
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+++ b/main/generated/torch.cuda.ipc_collect.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.ipc_collect<a class="headerlink" href="#torch-cuda-ipc-collect" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.ipc_collect">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">ipc_collect</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#ipc_collect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1002"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ipc_collect" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">ipc_collect</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#ipc_collect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1002"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ipc_collect" title="Permalink to this definition">¶</a></dt>
 <dd><p>Force collects GPU memory after it has been released by CUDA IPC.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.cuda.is_available.html b/main/generated/torch.cuda.is_available.html
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.cuda.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if CUDA is currently available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.is_current_stream_capturing.html b/main/generated/torch.cuda.is_current_stream_capturing.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.is_current_stream_capturing<a class="headerlink" href="#torch-cuda-is-current-stream-capturing" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.is_current_stream_capturing">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_current_stream_capturing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#is_current_stream_capturing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_current_stream_capturing" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_current_stream_capturing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#is_current_stream_capturing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_current_stream_capturing" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if CUDA graph capture is underway on the current CUDA stream, False otherwise.</p>
 <p>If a CUDA context does not exist on the current device, returns False without initializing the context.</p>
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diff --git a/main/generated/torch.cuda.is_initialized.html b/main/generated/torch.cuda.is_initialized.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.is_initialized<a class="headerlink" href="#torch-cuda-is-initialized" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_initialized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_initialized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch’s CUDA state has been initialized.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.cuda.is_tf32_supported.html b/main/generated/torch.cuda.is_tf32_supported.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.is_tf32_supported<a class="headerlink" href="#torch-cuda-is-tf32-supported" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_tf32_supported</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_tf32_supported"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_tf32_supported" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_tf32_supported</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_tf32_supported"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_tf32_supported" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if the current CUDA/ROCm device supports dtype tf32.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.jiterator._create_jit_fn<a class="headerlink" href="#torch-cuda-jiterator-create-jit-fn" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.jiterator._create_jit_fn">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.jiterator.</span></span><span class="sig-name descname"><span class="pre">_create_jit_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code_string</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/jiterator.html#_create_jit_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/jiterator.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.jiterator._create_jit_fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.jiterator.</span></span><span class="sig-name descname"><span class="pre">_create_jit_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code_string</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/jiterator.html#_create_jit_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/jiterator.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.jiterator._create_jit_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a jiterator-generated cuda kernel for an elementwise op.</p>
 <p>The code string has to be a valid CUDA function that describes the computation for a single element. The code
 string has to follow the c++ template pattern, as shown in the example below. This function will be inlined
diff --git a/main/generated/torch.cuda.jiterator._create_multi_output_jit_fn.html b/main/generated/torch.cuda.jiterator._create_multi_output_jit_fn.html
index afa68f56419..be200d3e1b1 100644
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+++ b/main/generated/torch.cuda.jiterator._create_multi_output_jit_fn.html
@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.jiterator._create_multi_output_jit_fn<a class="headerlink" href="#torch-cuda-jiterator-create-multi-output-jit-fn" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.jiterator.</span></span><span class="sig-name descname"><span class="pre">_create_multi_output_jit_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_outputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/jiterator.html#_create_multi_output_jit_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/jiterator.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.jiterator._create_multi_output_jit_fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.jiterator.</span></span><span class="sig-name descname"><span class="pre">_create_multi_output_jit_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_outputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/jiterator.html#_create_multi_output_jit_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/jiterator.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.jiterator._create_multi_output_jit_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a jiterator-generated cuda kernel for an elementwise op that supports returning one or more outputs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.list_gpu_processes.html b/main/generated/torch.cuda.list_gpu_processes.html
index 3d6627e1c3d..904148c647c 100644
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+++ b/main/generated/torch.cuda.list_gpu_processes.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.list_gpu_processes<a class="headerlink" href="#torch-cuda-list-gpu-processes" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.list_gpu_processes">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">list_gpu_processes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#list_gpu_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L657"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.list_gpu_processes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">list_gpu_processes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#list_gpu_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L657"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.list_gpu_processes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a human-readable printout of the running processes and their GPU memory use for a given device.</p>
 <p>This can be useful to display periodically during training, or when
 handling out-of-memory exceptions.</p>
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.make_graphed_callables<a class="headerlink" href="#torch-cuda-make-graphed-callables" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.make_graphed_callables">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">make_graphed_callables</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_warmup_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unused_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#make_graphed_callables"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/graphs.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.make_graphed_callables" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">make_graphed_callables</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_warmup_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unused_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#make_graphed_callables"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.make_graphed_callables" title="Permalink to this definition">¶</a></dt>
 <dd><p>Accept callables (functions or <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Module</span></code></a>s) and returns graphed versions.</p>
 <p>Each graphed callable’s forward pass runs its source callable’s
 forward CUDA work as a CUDA graph inside a single autograd node.</p>
diff --git a/main/generated/torch.cuda.manual_seed.html b/main/generated/torch.cuda.manual_seed.html
index e378602ba7b..362f57af4a8 100644
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+++ b/main/generated/torch.cuda.manual_seed.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.manual_seed<a class="headerlink" href="#torch-cuda-manual-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/random.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers for the current GPU.</p>
 <p>It’s safe to call this function if CUDA is not available; in that
 case, it is silently ignored.</p>
diff --git a/main/generated/torch.cuda.manual_seed_all.html b/main/generated/torch.cuda.manual_seed_all.html
index cb062e95ebd..c33301a811d 100644
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+++ b/main/generated/torch.cuda.manual_seed_all.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.manual_seed_all<a class="headerlink" href="#torch-cuda-manual-seed-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.manual_seed_all">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">manual_seed_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#manual_seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/random.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.manual_seed_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">manual_seed_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#manual_seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.manual_seed_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers on all GPUs.</p>
 <p>It’s safe to call this function if CUDA is not available; in that
 case, it is silently ignored.</p>
diff --git a/main/generated/torch.cuda.max_memory_allocated.html b/main/generated/torch.cuda.max_memory_allocated.html
index 448ed6d7336..bc7cd36ccf7 100644
--- a/main/generated/torch.cuda.max_memory_allocated.html
+++ b/main/generated/torch.cuda.max_memory_allocated.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.max_memory_allocated<a class="headerlink" href="#torch-cuda-max-memory-allocated" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.max_memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the maximum GPU memory occupied by tensors in bytes for a given device.</p>
 <p>By default, this returns the peak allocated memory since the beginning of
 this program. <a class="reference internal" href="torch.cuda.reset_peak_memory_stats.html#torch.cuda.reset_peak_memory_stats" title="torch.cuda.reset_peak_memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">reset_peak_memory_stats()</span></code></a> can be used to
diff --git a/main/generated/torch.cuda.max_memory_cached.html b/main/generated/torch.cuda.max_memory_cached.html
index f75bf0ffeb3..bb781aeda97 100644
--- a/main/generated/torch.cuda.max_memory_cached.html
+++ b/main/generated/torch.cuda.max_memory_cached.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.max_memory_cached<a class="headerlink" href="#torch-cuda-max-memory-cached" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.max_memory_cached">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_cached" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_cached" title="Permalink to this definition">¶</a></dt>
 <dd><p>Deprecated; see <a class="reference internal" href="torch.cuda.max_memory_reserved.html#torch.cuda.max_memory_reserved" title="torch.cuda.max_memory_reserved"><code class="xref py py-func docutils literal notranslate"><span class="pre">max_memory_reserved()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.max_memory_reserved.html b/main/generated/torch.cuda.max_memory_reserved.html
index 7f08d696d91..2935ecf129a 100644
--- a/main/generated/torch.cuda.max_memory_reserved.html
+++ b/main/generated/torch.cuda.max_memory_reserved.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.max_memory_reserved<a class="headerlink" href="#torch-cuda-max-memory-reserved" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.max_memory_reserved">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_reserved" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_reserved" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the maximum GPU memory managed by the caching allocator in bytes for a given device.</p>
 <p>By default, this returns the peak cached memory since the beginning of this
 program. <a class="reference internal" href="torch.cuda.reset_peak_memory_stats.html#torch.cuda.reset_peak_memory_stats" title="torch.cuda.reset_peak_memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">reset_peak_memory_stats()</span></code></a> can be used to reset
diff --git a/main/generated/torch.cuda.mem_get_info.html b/main/generated/torch.cuda.mem_get_info.html
index ec8e72087f9..7e69841fa7a 100644
--- a/main/generated/torch.cuda.mem_get_info.html
+++ b/main/generated/torch.cuda.mem_get_info.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.mem_get_info<a class="headerlink" href="#torch-cuda-mem-get-info" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.mem_get_info">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">mem_get_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#mem_get_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.mem_get_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">mem_get_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#mem_get_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.mem_get_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the global free and total GPU memory for a given device using cudaMemGetInfo.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.memory.caching_allocator_enable.html b/main/generated/torch.cuda.memory.caching_allocator_enable.html
index 2a7ab572c18..24ad5b5b051 100644
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+++ b/main/generated/torch.cuda.memory.caching_allocator_enable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory.caching_allocator_enable<a class="headerlink" href="#torch-cuda-memory-caching-allocator-enable" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory.caching_allocator_enable">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory.caching_allocator_enable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory.caching_allocator_enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable or disable the CUDA memory allocator. On by default.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.cuda.memory_allocated.html b/main/generated/torch.cuda.memory_allocated.html
index 7bfbd1461e0..3589c597817 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_allocated<a class="headerlink" href="#torch-cuda-memory-allocated" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current GPU memory occupied by tensors in bytes for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.memory_cached.html b/main/generated/torch.cuda.memory_cached.html
index 8969e62f538..8876ad3f8ad 100644
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+++ b/main/generated/torch.cuda.memory_cached.html
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     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_cached<a class="headerlink" href="#torch-cuda-memory-cached" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_cached">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_cached" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_cached" title="Permalink to this definition">¶</a></dt>
 <dd><p>Deprecated; see <a class="reference internal" href="torch.cuda.memory_reserved.html#torch.cuda.memory_reserved" title="torch.cuda.memory_reserved"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_reserved()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.memory_reserved.html b/main/generated/torch.cuda.memory_reserved.html
index dda8302fad3..c4d35935b57 100644
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+++ b/main/generated/torch.cuda.memory_reserved.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_reserved<a class="headerlink" href="#torch-cuda-memory-reserved" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_reserved">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L463"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_reserved" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L463"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_reserved" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current GPU memory managed by the caching allocator in bytes for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.memory_snapshot.html b/main/generated/torch.cuda.memory_snapshot.html
index 003e3f8c324..3e87c06db23 100644
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+++ b/main/generated/torch.cuda.memory_snapshot.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_snapshot<a class="headerlink" href="#torch-cuda-memory-snapshot" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_snapshot">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L517"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L517"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a snapshot of the CUDA memory allocator state across all devices.</p>
 <p>Interpreting the output of this function requires familiarity with the
 memory allocator internals.</p>
diff --git a/main/generated/torch.cuda.memory_stats.html b/main/generated/torch.cuda.memory_stats.html
index de089a817a5..190aa6794fd 100644
--- a/main/generated/torch.cuda.memory_stats.html
+++ b/main/generated/torch.cuda.memory_stats.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_stats<a class="headerlink" href="#torch-cuda-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of CUDA memory allocator statistics for a given device.</p>
 <p>The return value of this function is a dictionary of statistics, each of
 which is a non-negative integer.</p>
diff --git a/main/generated/torch.cuda.memory_summary.html b/main/generated/torch.cuda.memory_summary.html
index c997ea90b90..9c1d3bd3c3e 100644
--- a/main/generated/torch.cuda.memory_summary.html
+++ b/main/generated/torch.cuda.memory_summary.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_summary<a class="headerlink" href="#torch-cuda-memory-summary" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_summary">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_summary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abbreviated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_summary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abbreviated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_summary" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a human-readable printout of the current memory allocator statistics for a given device.</p>
 <p>This can be useful to display periodically during training, or when
 handling out-of-memory exceptions.</p>
diff --git a/main/generated/torch.cuda.memory_usage.html b/main/generated/torch.cuda.memory_usage.html
index 6358f4d8107..6af27a09c1b 100644
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+++ b/main/generated/torch.cuda.memory_usage.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_usage<a class="headerlink" href="#torch-cuda-memory-usage" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_usage">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_usage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#memory_usage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1239"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_usage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_usage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#memory_usage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1239"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_usage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the percent of time over the past sample period during which global (device)
 memory was being read or written as given by <cite>nvidia-smi</cite>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cuda.nvtx.mark.html b/main/generated/torch.cuda.nvtx.mark.html
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+++ b/main/generated/torch.cuda.nvtx.mark.html
@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.nvtx.mark<a class="headerlink" href="#torch-cuda-nvtx-mark" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.nvtx.mark">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">mark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#mark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/nvtx.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.mark" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">mark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#mark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/nvtx.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.mark" title="Permalink to this definition">¶</a></dt>
 <dd><p>Describe an instantaneous event that occurred at some point.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.nvtx.range.html b/main/generated/torch.cuda.nvtx.range.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.nvtx.range<a class="headerlink" href="#torch-cuda-nvtx-range" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.nvtx.range">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/nvtx.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/nvtx.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager / decorator that pushes an NVTX range at the beginning
 of its scope, and pops it at the end. If extra arguments are given,
 they are passed as arguments to msg.format().</p>
diff --git a/main/generated/torch.cuda.nvtx.range_pop.html b/main/generated/torch.cuda.nvtx.range_pop.html
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 <h1>torch.cuda.nvtx.range_pop<a class="headerlink" href="#torch-cuda-nvtx-range-pop" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.nvtx.range_pop">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range_pop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range_pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/nvtx.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range_pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range_pop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range_pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/nvtx.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range_pop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pop a range off of a stack of nested range spans.  Returns the  zero-based depth of the range that is ended.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.cuda.nvtx.range_push.html b/main/generated/torch.cuda.nvtx.range_push.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.nvtx.range_push<a class="headerlink" href="#torch-cuda-nvtx-range-push" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.nvtx.range_push">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range_push</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range_push"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/nvtx.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range_push" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range_push</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range_push"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/nvtx.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range_push" title="Permalink to this definition">¶</a></dt>
 <dd><p>Push a range onto a stack of nested range span.  Returns zero-based depth of the range that is started.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.power_draw.html b/main/generated/torch.cuda.power_draw.html
index 29c69f4aacd..1b1473385f3 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.power_draw<a class="headerlink" href="#torch-cuda-power-draw" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.power_draw">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">power_draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#power_draw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1302"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.power_draw" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">power_draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#power_draw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1302"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.power_draw" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Return the average power draw of the GPU sensor in mW (MilliWatts)</dt><dd><p>over the past sample period as given by <cite>nvidia-smi</cite> for Fermi or newer fully supported devices.</p>
 </dd>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.reset_max_memory_allocated<a class="headerlink" href="#torch-cuda-reset-max-memory-allocated" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.reset_max_memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L373"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_max_memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L373"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_max_memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the starting point in tracking maximum GPU memory occupied by tensors for a given device.</p>
 <p>See <a class="reference internal" href="torch.cuda.max_memory_allocated.html#torch.cuda.max_memory_allocated" title="torch.cuda.max_memory_allocated"><code class="xref py py-func docutils literal notranslate"><span class="pre">max_memory_allocated()</span></code></a> for details.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.reset_max_memory_cached<a class="headerlink" href="#torch-cuda-reset-max-memory-cached" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.reset_max_memory_cached">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_max_memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_max_memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_max_memory_cached" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_max_memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_max_memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_max_memory_cached" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the starting point in tracking maximum GPU memory managed by the caching allocator for a given device.</p>
 <p>See <a class="reference internal" href="torch.cuda.max_memory_cached.html#torch.cuda.max_memory_cached" title="torch.cuda.max_memory_cached"><code class="xref py py-func docutils literal notranslate"><span class="pre">max_memory_cached()</span></code></a> for details.</p>
 <dl class="field-list simple">
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index d8f3aa74a9c..2baa69ecfc3 100644
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@@ -300,7 +300,7 @@
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 <h1>torch.cuda.reset_peak_memory_stats<a class="headerlink" href="#torch-cuda-reset-peak-memory-stats" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.reset_peak_memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_peak_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_peak_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_peak_memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_peak_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_peak_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_peak_memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the “peak” stats tracked by the CUDA memory allocator.</p>
 <p>See <a class="reference internal" href="torch.cuda.memory_stats.html#torch.cuda.memory_stats" title="torch.cuda.memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> for details. Peak stats correspond to the
 <cite>“peak”</cite> key in each individual stat dict.</p>
diff --git a/main/generated/torch.cuda.seed.html b/main/generated/torch.cuda.seed.html
index e40403c1430..8ee9c2b6a7c 100644
--- a/main/generated/torch.cuda.seed.html
+++ b/main/generated/torch.cuda.seed.html
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.seed<a class="headerlink" href="#torch-cuda-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/random.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers to a random number for the current GPU.</p>
 <p>It’s safe to call this function if CUDA is not available; in that
 case, it is silently ignored.</p>
diff --git a/main/generated/torch.cuda.seed_all.html b/main/generated/torch.cuda.seed_all.html
index 83a30c64ed1..b9ee389b8f3 100644
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+++ b/main/generated/torch.cuda.seed_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.seed_all<a class="headerlink" href="#torch-cuda-seed-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.seed_all">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">seed_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/random.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.seed_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">seed_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.seed_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers to a random number on all GPUs.</p>
 <p>It’s safe to call this function if CUDA is not available; in that
 case, it is silently ignored.</p>
diff --git a/main/generated/torch.cuda.set_device.html b/main/generated/torch.cuda.set_device.html
index 8c57ba139bd..6aa6316272a 100644
--- a/main/generated/torch.cuda.set_device.html
+++ b/main/generated/torch.cuda.set_device.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_device<a class="headerlink" href="#torch-cuda-set-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_device">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device.</p>
 <p>Usage of this function is discouraged in favor of <a class="reference internal" href="torch.cuda.device.html#torch.cuda.device" title="torch.cuda.device"><code class="xref any py py-class docutils literal notranslate"><span class="pre">device</span></code></a>. In most
 cases it’s better to use <code class="docutils literal notranslate"><span class="pre">CUDA_VISIBLE_DEVICES</span></code> environmental variable.</p>
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index 8dc8fc90b90..a27b1c46e7f 100644
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+++ b/main/generated/torch.cuda.set_per_process_memory_fraction.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_per_process_memory_fraction<a class="headerlink" href="#torch-cuda-set-per-process-memory-fraction" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_per_process_memory_fraction">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fraction</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#set_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fraction</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#set_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set memory fraction for a process.</p>
 <p>The fraction is used to limit an caching allocator to allocated memory on a CUDA device.
 The allowed value equals the total visible memory multiplied fraction.
diff --git a/main/generated/torch.cuda.set_rng_state.html b/main/generated/torch.cuda.set_rng_state.html
index 87ae69c18e8..2f8f45f8d89 100644
--- a/main/generated/torch.cuda.set_rng_state.html
+++ b/main/generated/torch.cuda.set_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_rng_state<a class="headerlink" href="#torch-cuda-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/random.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the random number generator state of the specified GPU.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.set_rng_state_all.html b/main/generated/torch.cuda.set_rng_state_all.html
index cf302f278c9..ce606691d26 100644
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+++ b/main/generated/torch.cuda.set_rng_state_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_rng_state_all<a class="headerlink" href="#torch-cuda-set-rng-state-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_rng_state_all">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_rng_state_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#set_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/random.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_rng_state_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_rng_state_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#set_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_rng_state_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the random number generator state of all devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.set_stream.html b/main/generated/torch.cuda.set_stream.html
index a2049e156f0..afb3d621b97 100644
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+++ b/main/generated/torch.cuda.set_stream.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_stream<a class="headerlink" href="#torch-cuda-set-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_stream" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Set the current stream.This is a wrapper API to set the stream.</dt><dd><p>Usage of this function is discouraged in favor of the <code class="docutils literal notranslate"><span class="pre">stream</span></code>
 context manager.</p>
diff --git a/main/generated/torch.cuda.set_sync_debug_mode.html b/main/generated/torch.cuda.set_sync_debug_mode.html
index 48dbe66c70d..d3d44eeb55f 100644
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+++ b/main/generated/torch.cuda.set_sync_debug_mode.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_sync_debug_mode<a class="headerlink" href="#torch-cuda-set-sync-debug-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_sync_debug_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_sync_debug_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_sync_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1085"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_sync_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_sync_debug_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_sync_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1085"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_sync_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the debug mode for cuda synchronizing operations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.stream.html b/main/generated/torch.cuda.stream.html
index 50eb8bb901f..b382317216a 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.stream<a class="headerlink" href="#torch-cuda-stream" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap around the Context-manager StreamContext that selects a given stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.synchronize.html b/main/generated/torch.cuda.synchronize.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.synchronize<a class="headerlink" href="#torch-cuda-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L989"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L989"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all kernels in all streams on a CUDA device to complete.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.temperature.html b/main/generated/torch.cuda.temperature.html
index bcab6ec5e67..23046f6a689 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.temperature<a class="headerlink" href="#torch-cuda-temperature" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.temperature">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">temperature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#temperature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.temperature" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">temperature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#temperature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.temperature" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the average temperature of the GPU sensor in Degrees C (Centigrades).</p>
 <p>The average temperature is computed based on past sample period as given by <cite>nvidia-smi</cite>.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.cuda.utilization">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">utilization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#utilization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/__init__.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.utilization" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">utilization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#utilization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.utilization" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the percent of time over the past sample period during which one or
 more kernels was executing on the GPU as given by <cite>nvidia-smi</cite>.</p>
 <dl class="field-list simple">
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 <h1>torch.einsum<a class="headerlink" href="#torch-einsum" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.einsum">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">einsum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">equation</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">operands</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/functional.html#einsum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.einsum" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">einsum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">equation</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">operands</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/functional.html#einsum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.einsum" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sums the product of the elements of the input <code class="xref py py-attr docutils literal notranslate"><span class="pre">operands</span></code> along dimensions specified using a notation
 based on the Einstein summation convention.</p>
 <p>Einsum allows computing many common multi-dimensional linear algebraic array operations by representing them
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 <h1>enable_grad<a class="headerlink" href="#enable-grad" title="Permalink to this heading">¶</a></h1>
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-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">enable_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#enable_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/grad_mode.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.enable_grad" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">enable_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#enable_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.enable_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that enables gradient calculation.</p>
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index b7ea68d8adc..4a47c182478 100644
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diff --git a/main/generated/torch.frexp.html b/main/generated/torch.frexp.html
index 47a504844b0..b31c1f72369 100644
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diff --git a/main/generated/torch.from_dlpack.html b/main/generated/torch.from_dlpack.html
index 0ffb660abb6..85d35b26500 100644
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@@ -612,7 +612,7 @@
 <h1>torch.from_dlpack<a class="headerlink" href="#torch-from-dlpack" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.from_dlpack">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">from_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ext_tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/utils/dlpack.html#from_dlpack"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/dlpack.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.from_dlpack" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">from_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ext_tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/utils/dlpack.html#from_dlpack"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/dlpack.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.from_dlpack" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts a tensor from an external library into a <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>.</p>
 <p>The returned PyTorch tensor will share the memory with the input tensor
 (which may have come from another library). Note that in-place operations
diff --git a/main/generated/torch.from_file.html b/main/generated/torch.from_file.html
index 1d612ea6469..1a142b615f2 100644
--- a/main/generated/torch.from_file.html
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diff --git a/main/generated/torch.from_numpy.html b/main/generated/torch.from_numpy.html
index 778aec027db..49056776d72 100644
--- a/main/generated/torch.from_numpy.html
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diff --git a/main/generated/torch.frombuffer.html b/main/generated/torch.frombuffer.html
index 64e36dc1c26..0a559f74a74 100644
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diff --git a/main/generated/torch.full.html b/main/generated/torch.full.html
index ab5e3b632b5..77805b54cdf 100644
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diff --git a/main/generated/torch.full_like.html b/main/generated/torch.full_like.html
index 3ae5ce94d51..645c83a7a0e 100644
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diff --git a/main/generated/torch.func.functional_call.html b/main/generated/torch.func.functional_call.html
index 59d7f3c267e..686212c2c5c 100644
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@@ -614,7 +614,7 @@
 <h1>torch.func.functional_call<a class="headerlink" href="#torch-func-functional-call" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.functional_call">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">functional_call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_and_buffer_dicts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tie_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/functional_call.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.functional_call" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">functional_call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_and_buffer_dicts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tie_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/functional_call.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.functional_call" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs a functional call on the module by replacing the module parameters
 and buffers with the provided ones.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.func.functionalize.html b/main/generated/torch.func.functionalize.html
index 46477a3dfb8..29c336d5a2f 100644
--- a/main/generated/torch.func.functionalize.html
+++ b/main/generated/torch.func.functionalize.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>torch.func.functionalize<a class="headerlink" href="#torch-func-functionalize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.functionalize">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">functionalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mutations'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/eager_transforms.py#L1464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.functionalize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">functionalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mutations'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L1464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.functionalize" title="Permalink to this definition">¶</a></dt>
 <dd><p>functionalize is a transform that can be used to remove (intermediate)
 mutations and aliasing from a function, while preserving the function’s
 semantics.</p>
diff --git a/main/generated/torch.func.grad.html b/main/generated/torch.func.grad.html
index b091c32c582..2d26fc7d751 100644
--- a/main/generated/torch.func.grad.html
+++ b/main/generated/torch.func.grad.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.grad<a class="headerlink" href="#torch-func-grad" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.grad">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/apis.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/apis.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.grad" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">grad</span></code> operator helps computing gradients of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the
 input(s) specified by <code class="docutils literal notranslate"><span class="pre">argnums</span></code>. This operator can be nested to
 compute higher-order gradients.</p>
diff --git a/main/generated/torch.func.grad_and_value.html b/main/generated/torch.func.grad_and_value.html
index 5e428eab5c4..fe65b0a89f5 100644
--- a/main/generated/torch.func.grad_and_value.html
+++ b/main/generated/torch.func.grad_and_value.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.grad_and_value<a class="headerlink" href="#torch-func-grad-and-value" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.grad_and_value">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">grad_and_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/apis.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.grad_and_value" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">grad_and_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/apis.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.grad_and_value" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a function to compute a tuple of the gradient and primal, or
 forward, computation.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.func.hessian.html b/main/generated/torch.func.hessian.html
index ce022a76631..875b6068145 100644
--- a/main/generated/torch.func.hessian.html
+++ b/main/generated/torch.func.hessian.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.hessian<a class="headerlink" href="#torch-func-hessian" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.hessian">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">hessian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/eager_transforms.py#L1310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.hessian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">hessian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L1310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.hessian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Hessian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the arg(s) at index
 <code class="docutils literal notranslate"><span class="pre">argnum</span></code> via a forward-over-reverse strategy.</p>
 <p>The forward-over-reverse strategy (composing <code class="docutils literal notranslate"><span class="pre">jacfwd(jacrev(func))</span></code>) is
diff --git a/main/generated/torch.func.jacfwd.html b/main/generated/torch.func.jacfwd.html
index b85c98f267d..085d7431fd8 100644
--- a/main/generated/torch.func.jacfwd.html
+++ b/main/generated/torch.func.jacfwd.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.jacfwd<a class="headerlink" href="#torch-func-jacfwd" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.jacfwd">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jacfwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/eager_transforms.py#L1139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jacfwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jacfwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L1139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jacfwd" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the arg(s) at index
 <code class="docutils literal notranslate"><span class="pre">argnum</span></code> using forward-mode autodiff</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.func.jacrev.html b/main/generated/torch.func.jacrev.html
index 78317dff74d..d494c14d394 100644
--- a/main/generated/torch.func.jacrev.html
+++ b/main/generated/torch.func.jacrev.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.jacrev<a class="headerlink" href="#torch-func-jacrev" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.jacrev">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jacrev</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_preallocate_and_copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/eager_transforms.py#L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jacrev" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jacrev</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_preallocate_and_copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jacrev" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the arg(s) at index
 <code class="docutils literal notranslate"><span class="pre">argnum</span></code> using reverse mode autodiff</p>
 <div class="admonition note">
diff --git a/main/generated/torch.func.jvp.html b/main/generated/torch.func.jvp.html
index 913448e0794..95dd6be6643 100644
--- a/main/generated/torch.func.jvp.html
+++ b/main/generated/torch.func.jvp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.jvp<a class="headerlink" href="#torch-func-jvp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.jvp">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">primals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangents</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/eager_transforms.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jvp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">primals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangents</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Standing for the Jacobian-vector product, returns a tuple containing
 the output of <cite>func(*primals)</cite> and the “Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> evaluated at
 <code class="docutils literal notranslate"><span class="pre">primals</span></code>” times <code class="docutils literal notranslate"><span class="pre">tangents</span></code>. This is also known as forward-mode autodiff.</p>
diff --git a/main/generated/torch.func.linearize.html b/main/generated/torch.func.linearize.html
index b3872e63e13..35b60698288 100644
--- a/main/generated/torch.func.linearize.html
+++ b/main/generated/torch.func.linearize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.linearize<a class="headerlink" href="#torch-func-linearize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.linearize">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">linearize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">primals</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/eager_transforms.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.linearize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">linearize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">primals</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.linearize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the value of <code class="docutils literal notranslate"><span class="pre">func</span></code> at <code class="docutils literal notranslate"><span class="pre">primals</span></code> and linear approximation
 at <code class="docutils literal notranslate"><span class="pre">primals</span></code>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.func.replace_all_batch_norm_modules_.html b/main/generated/torch.func.replace_all_batch_norm_modules_.html
index c9f5fafd059..641cc086389 100644
--- a/main/generated/torch.func.replace_all_batch_norm_modules_.html
+++ b/main/generated/torch.func.replace_all_batch_norm_modules_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.replace_all_batch_norm_modules_<a class="headerlink" href="#torch-func-replace-all-batch-norm-modules" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.replace_all_batch_norm_modules_">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">replace_all_batch_norm_modules_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/batch_norm_replacement.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.replace_all_batch_norm_modules_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">replace_all_batch_norm_modules_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/batch_norm_replacement.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.replace_all_batch_norm_modules_" title="Permalink to this definition">¶</a></dt>
 <dd><p>In place updates <code class="xref py py-attr docutils literal notranslate"><span class="pre">root</span></code> by setting the <code class="docutils literal notranslate"><span class="pre">running_mean</span></code> and <code class="docutils literal notranslate"><span class="pre">running_var</span></code> to be None and
 setting track_running_stats to be False for any nn.BatchNorm module in <code class="xref py py-attr docutils literal notranslate"><span class="pre">root</span></code></p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.func.stack_module_state.html b/main/generated/torch.func.stack_module_state.html
index d43c1ccf179..5ce7999d4a2 100644
--- a/main/generated/torch.func.stack_module_state.html
+++ b/main/generated/torch.func.stack_module_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.stack_module_state<a class="headerlink" href="#torch-func-stack-module-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.stack_module_state">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">stack_module_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">models</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">params</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">buffers</span></span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/functional_call.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.stack_module_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">stack_module_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">models</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">params</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">buffers</span></span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/functional_call.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.stack_module_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepares a list of torch.nn.Modules for ensembling with <a class="reference internal" href="torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a>.</p>
 <p>Given a list of <code class="docutils literal notranslate"><span class="pre">M</span></code> <code class="docutils literal notranslate"><span class="pre">nn.Modules</span></code> of the same class, returns two dictionaries
 that stack all of their parameters and buffers together, indexed by name.
diff --git a/main/generated/torch.func.vjp.html b/main/generated/torch.func.vjp.html
index 24888b4d337..16f0b574f87 100644
--- a/main/generated/torch.func.vjp.html
+++ b/main/generated/torch.func.vjp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.vjp<a class="headerlink" href="#torch-func-vjp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.vjp">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">primals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/eager_transforms.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.vjp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">primals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.vjp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Standing for the vector-Jacobian product, returns a tuple containing the
 results of <code class="docutils literal notranslate"><span class="pre">func</span></code> applied to <code class="docutils literal notranslate"><span class="pre">primals</span></code> and a function that, when
 given <code class="docutils literal notranslate"><span class="pre">cotangents</span></code>, computes the reverse-mode Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with
diff --git a/main/generated/torch.func.vmap.html b/main/generated/torch.func.vmap.html
index 4a5edd26677..d4f4257ce13 100644
--- a/main/generated/torch.func.vmap.html
+++ b/main/generated/torch.func.vmap.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.vmap<a class="headerlink" href="#torch-func-vmap" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.vmap">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/apis.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.vmap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/apis.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>vmap is the vectorizing map; <code class="docutils literal notranslate"><span class="pre">vmap(func)</span></code> returns a new function that
 maps <code class="docutils literal notranslate"><span class="pre">func</span></code> over some dimension of the inputs. Semantically, vmap
 pushes the map into PyTorch operations called by <code class="docutils literal notranslate"><span class="pre">func</span></code>, effectively
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.get_proxy_mode.html b/main/generated/torch.fx.experimental.proxy_tensor.get_proxy_mode.html
index 9b5f5ba92c6..74c79b7029e 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.get_proxy_mode.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.get_proxy_mode.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.get_proxy_mode<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-get-proxy-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.get_proxy_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">get_proxy_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#get_proxy_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/proxy_tensor.py#L2247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.get_proxy_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">get_proxy_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#get_proxy_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L2209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.get_proxy_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Current the currently active proxy tracing mode, or None if
 we are not currently tracing.  This includes pre-dispatch proxy
 tracing.</p>
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.handle_sym_dispatch.html b/main/generated/torch.fx.experimental.proxy_tensor.handle_sym_dispatch.html
index 64bd15624ee..33ec72b0f06 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.handle_sym_dispatch.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.handle_sym_dispatch.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.handle_sym_dispatch<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-handle-sym-dispatch" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.handle_sym_dispatch">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">handle_sym_dispatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#handle_sym_dispatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/proxy_tensor.py#L2263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.handle_sym_dispatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">handle_sym_dispatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#handle_sym_dispatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L2225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.handle_sym_dispatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call into the currently active proxy tracing mode to do a
 SymInt/SymFloat/SymBool dispatch trace on a function that operates on
 these arguments.</p>
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.make_fx.html b/main/generated/torch.fx.experimental.proxy_tensor.make_fx.html
index 101a51ab6cb..d086d31c39b 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.make_fx.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.make_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.make_fx<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-make-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.make_fx">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">make_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decomposition_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracing_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'real'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_allow_non_fake_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_module_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_allow_fake_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_error_on_data_dependent_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#make_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/proxy_tensor.py#L2201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.make_fx" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">make_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decomposition_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracing_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'real'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_allow_non_fake_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_module_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_allow_fake_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_error_on_data_dependent_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#make_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L2163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.make_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a function f, return a new function which when executed with valid
 arguments to f, returns an FX GraphModule representing the set of operations that
 were executed during the course of execution.</p>
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html b/main/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html
index 448437feada..8439b11c3ea 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.maybe_disable_thunkify<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-maybe-disable-thunkify" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.maybe_disable_thunkify">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">maybe_disable_thunkify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#maybe_disable_thunkify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/proxy_tensor.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.maybe_disable_thunkify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">maybe_disable_thunkify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#maybe_disable_thunkify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L444"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.maybe_disable_thunkify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Within a context, disable thunkification.  See <a class="reference internal" href="torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html#torch.fx.experimental.proxy_tensor.maybe_enable_thunkify" title="torch.fx.experimental.proxy_tensor.maybe_enable_thunkify"><code class="xref py py-func docutils literal notranslate"><span class="pre">maybe_enable_thunkify()</span></code></a>
 for more details.  This is helpful if you have a wrapper function which
 you want to enable thunkification on, but in some segment on the inside (say,
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html b/main/generated/torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html
index 6dc93a819ff..a2feb933b52 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.maybe_enable_thunkify<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-maybe-enable-thunkify" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.maybe_enable_thunkify">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">maybe_enable_thunkify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#maybe_enable_thunkify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/proxy_tensor.py#L464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.maybe_enable_thunkify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">maybe_enable_thunkify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#maybe_enable_thunkify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L460"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.maybe_enable_thunkify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Within this context manager, if you are doing make_fx tracing, we will thunkify
 all SymNode compute and avoid tracing it into the graph unless it is actually needed.
 You should prefer to avoid using this as much as possible, as lazy evaluation of
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.CallMethodKey.html b/main/generated/torch.fx.experimental.symbolic_shapes.CallMethodKey.html
index bf847dc9100..d927caa5221 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.CallMethodKey.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.CallMethodKey.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>CallMethodKey<a class="headerlink" href="#callmethodkey" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.CallMethodKey">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">CallMethodKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'str'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#CallMethodKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L902"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.CallMethodKey" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">CallMethodKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'str'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#CallMethodKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L902"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.CallMethodKey" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.CallMethodKey.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#CallMethodKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L909"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.CallMethodKey.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#CallMethodKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L909"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.CallMethodKey.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call the method on object</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.ConvertIntKey.html b/main/generated/torch.fx.experimental.symbolic_shapes.ConvertIntKey.html
index 7d24f6f7a01..463e1836997 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.ConvertIntKey.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.ConvertIntKey.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>ConvertIntKey<a class="headerlink" href="#convertintkey" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ConvertIntKey">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ConvertIntKey</span></span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ConvertIntKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L892"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ConvertIntKey" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ConvertIntKey</span></span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ConvertIntKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L892"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ConvertIntKey" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ConvertIntKey.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ConvertIntKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ConvertIntKey.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ConvertIntKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ConvertIntKey.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the int value from bool</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html b/main/generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html
index ac200f9f63f..7083c0c7f4a 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,14 +612,14 @@
 <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DimConstraints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbol_to_source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_to_val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">marked_dynamic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_name_to_debug_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L2322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DimConstraints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbol_to_source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_to_val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">marked_dynamic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_name_to_debug_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints" title="Permalink to this definition">¶</a></dt>
 <dd><p>Custom solver for a system of constraints on symbolic dimensions.
 Solutions are “static” values or simplified “dynamic” constraints.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.add">
-<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L2480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.add" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.add" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add an expression to the set of constraints.</p>
 <p>Return whether the expression is a trivial constraint (i.e., an obvious tautology).</p>
 <dl class="field-list simple">
@@ -631,7 +631,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.add_equality">
-<span class="sig-name descname"><span class="pre">add_equality</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.add_equality"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L2525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.add_equality" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_equality</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.add_equality"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.add_equality" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add an equality constraint</p>
 <dl class="field-list simple">
 </dl>
@@ -639,7 +639,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.forced_specializations">
-<span class="sig-name descname"><span class="pre">forced_specializations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.forced_specializations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L2696"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.forced_specializations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forced_specializations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.forced_specializations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2696"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.forced_specializations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a dictionary of the names of symbols to their specialized value</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -650,7 +650,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.prettify_results">
-<span class="sig-name descname"><span class="pre">prettify_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_signature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_violation_error</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forced_specializations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.prettify_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L2903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.prettify_results" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prettify_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_signature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_violation_error</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forced_specializations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.prettify_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.prettify_results" title="Permalink to this definition">¶</a></dt>
 <dd><p>Format a message for constraint violation erros</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -661,7 +661,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.rewrite_with_congruences">
-<span class="sig-name descname"><span class="pre">rewrite_with_congruences</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">s</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.rewrite_with_congruences"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L2389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.rewrite_with_congruences" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rewrite_with_congruences</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">s</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.rewrite_with_congruences"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.rewrite_with_congruences" title="Permalink to this definition">¶</a></dt>
 <dd><p>Eliminate expressions of the form b // d and b % d while adding congruences of the form b % d == k.
 This leaves rational operators (in particular of the form b / d) that our inequality solver can handle.
 We solve the added congruences separately (using our congruence solver, see below).</p>
@@ -674,7 +674,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.solve">
-<span class="sig-name descname"><span class="pre">solve</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.solve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L2588"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.solve" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">solve</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.solve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2588"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.solve" title="Permalink to this definition">¶</a></dt>
 <dd><p>Solve the system of constraint equations to find simplified constraints</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html b/main/generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html
index 6a16608ed80..f1ed62ae37b 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>DimDynamic<a class="headerlink" href="#dimdynamic" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimDynamic">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DimDynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimDynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimDynamic" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DimDynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimDynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimDynamic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Controls how to perform symbol allocation for a dimension.  It is always
 sound to default this to DYNAMIC, but the policies DUCK and STATIC can
 result in better trace-time and compile-time performance, as they reduce
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.DivideByKey.html b/main/generated/torch.fx.experimental.symbolic_shapes.DivideByKey.html
index a8b6c9d2d05..d89706a9610 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.DivideByKey.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.DivideByKey.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>DivideByKey<a class="headerlink" href="#dividebykey" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DivideByKey">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DivideByKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">divisor</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Union[int,</span> <span class="pre">SymInt]'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DivideByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L926"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DivideByKey" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DivideByKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">divisor</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Union[int,</span> <span class="pre">SymInt]'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DivideByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L926"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DivideByKey" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DivideByKey.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DivideByKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DivideByKey.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DivideByKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DivideByKey.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Divide object by divisor</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html b/main/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html
index c0ef04e1e9f..12ef1bf7350 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>EqualityConstraint<a class="headerlink" href="#equalityconstraint" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.EqualityConstraint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">EqualityConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_pairs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">derived_equalities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phantom_symbols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relaxed_sources</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#EqualityConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.EqualityConstraint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">EqualityConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_pairs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">derived_equalities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phantom_symbols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relaxed_sources</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#EqualityConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.EqualityConstraint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent and decide various kinds of equality constraints between input sources.</p>
 <p>A “source pair” is a pair of input sources for dynamic dimensions that
 are specified equal. We represent <cite>source_pairs</cite> in a union-find forest
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html b/main/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html
index e0baf3abe4d..bd56040d01d 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>InnerTensorKey<a class="headerlink" href="#innertensorkey" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.InnerTensorKey">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">InnerTensorKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inner_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'str'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#InnerTensorKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.InnerTensorKey" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">InnerTensorKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inner_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'str'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#InnerTensorKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.InnerTensorKey" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.InnerTensorKey.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#InnerTensorKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L921"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.InnerTensorKey.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#InnerTensorKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L921"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.InnerTensorKey.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the inner tensor attribute</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html b/main/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html
index a5cd1a20cdc..1c77ec8133a 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymints" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">PropagateUnbackedSymInts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">garbage_collect_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#PropagateUnbackedSymInts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L6937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">PropagateUnbackedSymInts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">garbage_collect_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#PropagateUnbackedSymInts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L6937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.boxed_run">
-<span class="sig-name descname"><span class="pre">boxed_run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args_list</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.boxed_run" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">boxed_run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args_list</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.boxed_run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run <cite>module</cite> via interpretation and return the result.  This uses the “boxed”
 calling convention, where you pass a list of arguments, which will be cleared
 by the interpreter.  This ensures that input tensors are promptly deallocated.</p>
@@ -629,7 +629,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_function">
-<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_function</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -657,7 +657,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_method">
-<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_method</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -685,7 +685,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_module</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -713,7 +713,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_args_kwargs_from_env">
-<span class="sig-name descname"><span class="pre">fetch_args_kwargs_from_env</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_args_kwargs_from_env" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fetch_args_kwargs_from_env</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_args_kwargs_from_env" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fetch the concrete values of <code class="docutils literal notranslate"><span class="pre">args</span></code> and <code class="docutils literal notranslate"><span class="pre">kwargs</span></code> of node <code class="docutils literal notranslate"><span class="pre">n</span></code>
 from the current execution environment.</p>
 <dl class="field-list simple">
@@ -735,7 +735,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_attr">
-<span class="sig-name descname"><span class="pre">fetch_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fetch_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fetch an attribute from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy of <code class="docutils literal notranslate"><span class="pre">self.module</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -756,7 +756,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.get_attr">
-<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.get_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.get_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node. Will retrieve an attribute
 value from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy of <code class="docutils literal notranslate"><span class="pre">self.module</span></code>.</p>
 <dl class="field-list simple">
@@ -784,7 +784,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.map_nodes_to_values">
-<span class="sig-name descname"><span class="pre">map_nodes_to_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.map_nodes_to_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">map_nodes_to_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.map_nodes_to_values" title="Permalink to this definition">¶</a></dt>
 <dd><p>Recursively descend through <code class="docutils literal notranslate"><span class="pre">args</span></code> and look up the concrete value
 for each <code class="docutils literal notranslate"><span class="pre">Node</span></code> in the current execution environment.</p>
 <dl class="field-list simple">
@@ -806,7 +806,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.output">
-<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.output" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.output" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute an <code class="docutils literal notranslate"><span class="pre">output</span></code> node. This really just retrieves
 the value referenced by the <code class="docutils literal notranslate"><span class="pre">output</span></code> node and returns it.</p>
 <dl class="field-list simple">
@@ -834,7 +834,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.placeholder">
-<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.placeholder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.placeholder" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> node. Note that this is stateful:
 <code class="docutils literal notranslate"><span class="pre">Interpreter</span></code> maintains an internal iterator over
 arguments passed to <code class="docutils literal notranslate"><span class="pre">run</span></code> and this method returns
@@ -864,7 +864,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run">
-<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_env</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_io_processing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/interpreter.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_env</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_io_processing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run <cite>module</cite> via interpretation and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -893,7 +893,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run_node">
-<span class="sig-name descname"><span class="pre">run_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#PropagateUnbackedSymInts.run_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L6938"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#PropagateUnbackedSymInts.run_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L6938"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run an FX node, propagating unbacked Symbol bindings to the new fake tensor</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html b/main/generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html
index 27f71ae3ec5..42842457289 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RelaxedUnspecConstraint<a class="headerlink" href="#relaxedunspecconstraint" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">RelaxedUnspecConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#RelaxedUnspecConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">RelaxedUnspecConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#RelaxedUnspecConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint" title="Permalink to this definition">¶</a></dt>
 <dd><p>For clients: no explicit constraint; constraint is whatever is implicitly
 inferred by guards from tracing.</p>
 <p>For backends: there must exist at least TWO possible values for the
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html b/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html
index 9b055a5f31a..bd5cf39c823 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapeEnv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_record_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracked_fakes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L3081"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapeEnv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_record_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracked_fakes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3081"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.add_var_to_val">
-<span class="sig-name descname"><span class="pre">add_var_to_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.add_var_to_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4595"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.add_var_to_val" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_var_to_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.add_var_to_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4595"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.add_var_to_val" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds a new symbol to the symbolic environment.</p>
 <dl class="field-list simple">
 </dl>
@@ -625,7 +625,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.bind_symbols">
-<span class="sig-name descname"><span class="pre">bind_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.bind_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.bind_symbols" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bind_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.bind_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.bind_symbols" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a paired list of placeholders (fake tensors with
 symbolic sizes) and concrete arguments (regular tensors
 with real sizes), returns a dictionary mapping each
@@ -648,7 +648,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.bound_sympy">
-<span class="sig-name descname"><span class="pre">bound_sympy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_oblivious</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.bound_sympy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5545"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.bound_sympy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bound_sympy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_oblivious</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.bound_sympy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5545"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.bound_sympy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a sympy expression, computes a ValueRanges bound for what values it can be</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -659,7 +659,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.check_equal">
-<span class="sig-name descname"><span class="pre">check_equal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.check_equal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L3414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.check_equal" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">check_equal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.check_equal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.check_equal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compare another ShapeEnv for equivalence</p>
 <dl class="field-list simple">
 </dl>
@@ -667,7 +667,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.cleanup">
-<span class="sig-name descname"><span class="pre">cleanup</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.cleanup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L6746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.cleanup" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cleanup</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.cleanup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L6746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.cleanup" title="Permalink to this definition">¶</a></dt>
 <dd><p>Break reference cycles.</p>
 <p>This destroys the stacks. If you really want to keep them, we
 just need some way to break references on code objects.</p>
@@ -677,7 +677,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbol">
-<span class="sig-name descname"><span class="pre">create_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DimDynamic.DUCK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_not_specialize_zero_one</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbol" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DimDynamic.DUCK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_not_specialize_zero_one</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbol" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new symbol which is tracked by this ShapeEnv</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -688,7 +688,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbolic_sizes_strides_storage_offset">
-<span class="sig-name descname"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symbolic_sizes_strides_storage_offset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L3847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbolic_sizes_strides_storage_offset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symbolic_sizes_strides_storage_offset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbolic_sizes_strides_storage_offset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of symbolic sizes and strides for the given tensor.
 We try our best to express stride in terms of the sizes, so as to not
 introduce new symbolic variables.</p>
@@ -701,7 +701,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symboolnode">
-<span class="sig-name descname"><span class="pre">create_symboolnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symboolnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symboolnode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symboolnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symboolnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symboolnode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a SymBool object from a sympy boolean expression</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -712,7 +712,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symfloatnode">
-<span class="sig-name descname"><span class="pre">create_symfloatnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symfloatnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symfloatnode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symfloatnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symfloatnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symfloatnode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a SymFloat value from a symbolic expression</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -723,7 +723,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symintnode">
-<span class="sig-name descname"><span class="pre">create_symintnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symintnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symintnode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symintnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symintnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symintnode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a SymInt value from a symbolic expression</p>
 <p>If you know what the current hint value of the SymInt to be created
 is, pass it into hint.  Otherwise, pass None and we will make our best
@@ -737,7 +737,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symbool">
-<span class="sig-name descname"><span class="pre">create_unbacked_symbool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symbool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symbool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unbacked_symbool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symbool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symbool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a symbolic boolean without a hint value</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -748,7 +748,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symfloat">
-<span class="sig-name descname"><span class="pre">create_unbacked_symfloat</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symfloat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symfloat" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unbacked_symfloat</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symfloat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symfloat" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a symbolic float without a hint value</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -759,7 +759,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symint">
-<span class="sig-name descname"><span class="pre">create_unbacked_symint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unbacked_symint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a symbolic integer without a hint value</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -770,7 +770,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symbol">
-<span class="sig-name descname"><span class="pre">create_unspecified_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DimDynamic.DUCK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unspecified_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symbol" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unspecified_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DimDynamic.DUCK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unspecified_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symbol" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a symbol with an unspecified value</p>
 <p>Compared to standard symbols we do not assume the value is positive,
 nor do we specialze on zero or one values.</p>
@@ -783,7 +783,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symint_and_symbol">
-<span class="sig-name descname"><span class="pre">create_unspecified_symint_and_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unspecified_symint_and_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symint_and_symbol" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unspecified_symint_and_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unspecified_symint_and_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symint_and_symbol" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a SymInt wrapping a new unspecified symbol</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -794,7 +794,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.defer_runtime_assert">
-<span class="sig-name descname"><span class="pre">defer_runtime_assert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fx_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.defer_runtime_assert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L6759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.defer_runtime_assert" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">defer_runtime_assert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fx_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.defer_runtime_assert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L6759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.defer_runtime_assert" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create an assert that is checked at runtime</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -813,7 +813,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.deserialize_symexpr">
-<span class="sig-name descname"><span class="pre">deserialize_symexpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.deserialize_symexpr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.deserialize_symexpr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">deserialize_symexpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.deserialize_symexpr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.deserialize_symexpr" title="Permalink to this definition">¶</a></dt>
 <dd><p>To be used by compile_fx to deserialize symexprs</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -824,7 +824,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression">
-<span class="sig-name descname"><span class="pre">evaluate_guards_expression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_guards_expression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">evaluate_guards_expression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_guards_expression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression" title="Permalink to this definition">¶</a></dt>
 <dd><p>Expected to be used with produce_guards_expression(). Evaluates an expression
 generated by produce_guards_expression for the given concrete args.</p>
 <dl class="field-list simple">
@@ -836,7 +836,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_for_args">
-<span class="sig-name descname"><span class="pre">evaluate_guards_for_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_guards_for_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_for_args" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">evaluate_guards_for_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_guards_for_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_for_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generate guards for a graph’s placeholder values and evaluate the guards with args</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -847,7 +847,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_symexpr">
-<span class="sig-name descname"><span class="pre">evaluate_symexpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_symexpr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_symexpr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">evaluate_symexpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_symexpr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_symexpr" title="Permalink to this definition">¶</a></dt>
 <dd><p>To be used by compile_fx to evaluate symexprs</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -858,7 +858,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.format_guards">
-<span class="sig-name descname"><span class="pre">format_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.format_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5537"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.format_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">format_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.format_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5537"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.format_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Format this shape env’s guard expressions with optional traceback info if verbose</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -869,7 +869,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze">
-<span class="sig-name descname"><span class="pre">freeze</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.freeze"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L3639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">freeze</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.freeze"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze" title="Permalink to this definition">¶</a></dt>
 <dd><p>Freeze this ShapeEnv to stop accumulating guards</p>
 <p>A frozen ShapeEnv will ignore any further guards generated on it and
 only emit a warning which may lead to accuracy problems.</p>
@@ -879,7 +879,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze_runtime_asserts">
-<span class="sig-name descname"><span class="pre">freeze_runtime_asserts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.freeze_runtime_asserts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L3648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze_runtime_asserts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">freeze_runtime_asserts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.freeze_runtime_asserts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze_runtime_asserts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Freeze this ShapeEnv to stop adding deferred runtime asserts.</p>
 <p>We will error if you try to install a new runtime assert when it is
 frozen.  This would indicate a lowering violation, or perhaps something
@@ -891,7 +891,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.get_axioms">
-<span class="sig-name descname"><span class="pre">get_axioms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_hint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_axioms"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5563"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_axioms" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_axioms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_hint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_axioms"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5563"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_axioms" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given the symbols in an expression, it returns all the runtime asserts that have those symbols
 concatenated with all the guards.
 If symbols is None, it returns all the runtime asserts (and all the guards)</p>
@@ -904,7 +904,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.get_implications">
-<span class="sig-name descname"><span class="pre">get_implications</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">e</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_implications"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_implications" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_implications</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">e</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_implications"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_implications" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a expression, it returns a list of predicates that follow from it</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -915,7 +915,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.get_nontrivial_guards">
-<span class="sig-name descname"><span class="pre">get_nontrivial_guards</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_nontrivial_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_nontrivial_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_nontrivial_guards</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_nontrivial_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_nontrivial_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of guard expressions that aren’t statically known (i.e. not trivial)</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -926,7 +926,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.get_pruned_guards">
-<span class="sig-name descname"><span class="pre">get_pruned_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symints</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_pruned_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_pruned_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_pruned_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symints</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_pruned_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_pruned_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get a list of guards, but pruned so it only provides guards that
 reference symints from the passed in input</p>
 <dl class="field-list simple">
@@ -938,7 +938,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.ignore_fresh_unbacked_symbols">
-<span class="sig-name descname"><span class="pre">ignore_fresh_unbacked_symbols</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.ignore_fresh_unbacked_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L3627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.ignore_fresh_unbacked_symbols" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ignore_fresh_unbacked_symbols</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.ignore_fresh_unbacked_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.ignore_fresh_unbacked_symbols" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicates that the newly allocated unbacked SymInts are being
 discarded</p>
 <dl class="field-list simple">
@@ -950,7 +950,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.is_unbacked_symint">
-<span class="sig-name descname"><span class="pre">is_unbacked_symint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbol</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.is_unbacked_symint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.is_unbacked_symint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_unbacked_symint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbol</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.is_unbacked_symint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.is_unbacked_symint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if a sympy symbol matches the naming convention for unbacked symbols</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -961,7 +961,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards">
-<span class="sig-name descname"><span class="pre">produce_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">produce_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like produce_guards_verbose, but only returns the non-verbose python guard expressions
 (no verbose guards produced.)</p>
 <dl class="field-list simple">
@@ -973,7 +973,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_expression">
-<span class="sig-name descname"><span class="pre">produce_guards_expression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards_expression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_expression" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">produce_guards_expression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards_expression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_expression" title="Permalink to this definition">¶</a></dt>
 <dd><p>Expected to be used with evaluate_guards_expression(). Produces the guards
 for the given placeholders and returns a string expression to be evaluated
 by evaluate_guards_expression given concrete values for the placeholders.</p>
@@ -986,7 +986,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_verbose">
-<span class="sig-name descname"><span class="pre">produce_guards_verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_ref=&lt;function</span> <span class="pre">ShapeEnv.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_contexts=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equalities_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_simplified=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">langs=('python'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'verbose_python')</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards_verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L4634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_verbose" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">produce_guards_verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_ref=&lt;function</span> <span class="pre">ShapeEnv.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_contexts=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equalities_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_simplified=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">langs=('python'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'verbose_python')</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards_verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_verbose" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a list of guards strings which, when evaluated in a context that
 defines tensors for all the sources, returns True or False depending
 on if the guards in the list evaluated to True or not.  Primarily used by Dynamo,
@@ -1011,7 +1011,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.replace">
-<span class="sig-name descname"><span class="pre">replace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.replace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.replace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.replace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.replace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply symbol replacements to any symbols in the given expression</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1022,7 +1022,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.set_unbacked_var_to_val">
-<span class="sig-name descname"><span class="pre">set_unbacked_var_to_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.set_unbacked_var_to_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L3513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.set_unbacked_var_to_val" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_unbacked_var_to_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.set_unbacked_var_to_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.set_unbacked_var_to_val" title="Permalink to this definition">¶</a></dt>
 <dd><p>Used only when propagate_real_tensors; registers a value for an
 unbacked symbol, which can be used last resort to resolve hints.</p>
 <dl class="field-list simple">
@@ -1031,7 +1031,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.simplify">
-<span class="sig-name descname"><span class="pre">simplify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.simplify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.simplify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">simplify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.simplify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.simplify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Use known constraints and replacements to simplify the given expr</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1042,7 +1042,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.size_hint">
-<span class="sig-name descname"><span class="pre">size_hint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.size_hint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L5797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.size_hint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">size_hint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.size_hint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.size_hint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gets a size hint for a given expression from the underlying shapes we had.
 Does not introduce a guard, so only use this when you can guarantee that
 your code is still valid for arbitrary shapes (such as optimization decisions)</p>
@@ -1055,7 +1055,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.suppress_guards">
-<span class="sig-name descname"><span class="pre">suppress_guards</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.suppress_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L3775"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.suppress_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">suppress_guards</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.suppress_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3775"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.suppress_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager to ignore all guards generated inside</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnvSettings.html b/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnvSettings.html
index a39c7f72b89..40268b6c269 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnvSettings.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnvSettings.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ShapeEnvSettings<a class="headerlink" href="#shapeenvsettings" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnvSettings">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapeEnvSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">allow_scalar_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_dynamic_output_shape_ops</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_static_by_default</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">specialize_zero_one</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duck_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefer_deferred_runtime_asserts_over_guards</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_complex_guards_as_runtime_asserts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnvSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L3046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnvSettings" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapeEnvSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">allow_scalar_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_dynamic_output_shape_ops</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_static_by_default</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">specialize_zero_one</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duck_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefer_deferred_runtime_asserts_over_guards</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_complex_guards_as_runtime_asserts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnvSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnvSettings" title="Permalink to this definition">¶</a></dt>
 <dd><p>Encapsulates all shape env settings that could potentially affect
 FakeTensor dispatch. Used when creating dispatch cache keys.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html b/main/generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html
index a24c85150a9..734034a7a67 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StatefulSymbolicContext<a class="headerlink" href="#statefulsymboliccontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StatefulSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_env_to_source_to_symbol_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StatefulSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1807"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StatefulSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_env_to_source_to_symbol_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StatefulSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1807"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code> via
 a symbolic_context determination as given by a cache of Source:Symbol. A cache hit
 will reuse a stored symbol, and a cache miss will write to this cache.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html b/main/generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html
index 98528246b9c..2179f99c9bc 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StatelessSymbolicContext<a class="headerlink" href="#statelesssymboliccontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StatelessSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StatelessSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StatelessSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StatelessSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code> via
 a symbolic_context determination as given by <code class="docutils literal notranslate"><span class="pre">DimDynamic</span></code> and <code class="docutils literal notranslate"><span class="pre">DimConstraint</span></code>.
 This will cause fresh symbols to be allocated</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html b/main/generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html
index 6bd6a4e08b5..8066b87beab 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StrictMinMaxConstraint<a class="headerlink" href="#strictminmaxconstraint" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StrictMinMaxConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StrictMinMaxConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1531"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StrictMinMaxConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StrictMinMaxConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1531"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint" title="Permalink to this definition">¶</a></dt>
 <dd><p>For clients: the size at this dimension must be within ‘vr’ (which
 specifies a lower and upper bound, inclusive-inclusive) AND it
 must be non-negative and should not be 0 or 1 (but see NB below).</p>
@@ -630,7 +630,7 @@ <h1>StrictMinMaxConstraint<a class="headerlink" href="#strictminmaxconstraint" t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.render">
-<span class="sig-name descname"><span class="pre">render</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StrictMinMaxConstraint.render"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.render" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">render</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StrictMinMaxConstraint.render"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.render" title="Permalink to this definition">¶</a></dt>
 <dd><p>Format the constrain equation</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html b/main/generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html
index 341e8dd948c..d51081176b7 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SubclassSymbolicContext<a class="headerlink" href="#subclasssymboliccontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">SubclassSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_env_to_source_to_symbol_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_contexts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#SubclassSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">SubclassSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_env_to_source_to_symbol_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_contexts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#SubclassSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>The correct symbolic context for a given inner tensor of a traceable tensor subclass
 may differ from that of the outer symbolic context. This structure allows for this
 flexibility, with inner symbolic contexts mapped via attr -&gt; symbolic context.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html b/main/generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html
index 84e21297350..db3b9cd65ba 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SymbolicContext<a class="headerlink" href="#symboliccontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.SymbolicContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">SymbolicContext</span></span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#SymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1733"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.SymbolicContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">SymbolicContext</span></span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#SymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1733"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.SymbolicContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Data structure specifying how we should create symbols in
 <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code>; e.g., should
 they be static or dynamic.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html b/main/generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html
index 2be3e19d314..c3ca781e547 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-canonicalize-bool-expr" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">canonicalize_bool_expr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#canonicalize_bool_expr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">canonicalize_bool_expr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#canonicalize_bool_expr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Canonicalize a boolean expression by transforming it into a lt / le
 inequality and moving all the non-constant terms to the rhs.
 We canonicalize And / Ors / Not via cnf and then canonicalize their subexpr
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.check_consistent.html b/main/generated/torch.fx.experimental.symbolic_shapes.check_consistent.html
index 91ad8390ee5..4a77e43d25a 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.check_consistent.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.check_consistent.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.check_consistent<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-check-consistent" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.check_consistent">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">check_consistent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#check_consistent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L424"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.check_consistent" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">check_consistent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#check_consistent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L424"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.check_consistent" title="Permalink to this definition">¶</a></dt>
 <dd><p>Test that two “meta” values (typically either Tensor or SymInt) have
 the same values, e.g., after retracing.  If we don’t understand the
 quantities in question, we’ll just skip the consistency check.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings.html b/main/generated/torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings.html
index 3df23f83853..08c466fee3d 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-compute-unbacked-bindings" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">compute_unbacked_bindings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old_example_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peek</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#compute_unbacked_bindings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L938"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">compute_unbacked_bindings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old_example_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peek</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#compute_unbacked_bindings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L938"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings" title="Permalink to this definition">¶</a></dt>
 <dd><p>After having run fake tensor propagation and producing example_value
 result, traverse example_value looking for freshly bound unbacked
 symbols and record their paths for later.  It is an error if
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.constrain_range.html b/main/generated/torch.fx.experimental.symbolic_shapes.constrain_range.html
index 8e86406f85d..1bdff868932 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.constrain_range.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.constrain_range.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.constrain_range<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-constrain-range" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.constrain_range">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">constrain_range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#constrain_range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.constrain_range" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">constrain_range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#constrain_range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.constrain_range" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a constraint that the passed in SymInt must lie between min-max
 inclusive-inclusive, WITHOUT introducing a guard on the SymInt (meaning
 that it can be used on unbacked SymInts).  If min/max are None, we assume
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html b/main/generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html
index fac8ddb16a7..becdb8f02b8 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.constrain_unify<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-constrain-unify" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.constrain_unify">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">constrain_unify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#constrain_unify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.constrain_unify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">constrain_unify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#constrain_unify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.constrain_unify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given two SymInts, constrain them so that they must be equal.  NB:
 this will not work with SymInts that represent nontrivial expressions
 (yet!)</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.definitely_false.html b/main/generated/torch.fx.experimental.symbolic_shapes.definitely_false.html
index c48f4d39799..5b01cb64d1d 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.definitely_false.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.definitely_false.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.definitely_false<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-definitely-false" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.definitely_false">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">definitely_false</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#definitely_false"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.definitely_false" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">definitely_false</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#definitely_false"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.definitely_false" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True only if we can tell that a is False, possibly introducing
 a guard in the process.  If a depends on some unbacked SymInt, we may
 return False even though there may exist a possible value of the SymInt
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.definitely_true.html b/main/generated/torch.fx.experimental.symbolic_shapes.definitely_true.html
index 70930821ccd..6d3b7533a95 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.definitely_true.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.definitely_true.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.definitely_true<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-definitely-true" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.definitely_true">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">definitely_true</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#definitely_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.definitely_true" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">definitely_true</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#definitely_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.definitely_true" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True only if we can tell that a is True, possibly introducing
 a guard in the process.  If a depends on some unbacked SymInt, we may
 return False even though there may exist a possible value of the SymInt
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html b/main/generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html
index 0de9c3b294e..39d98078549 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.guard_size_oblivious<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-guard-size-oblivious" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.guard_size_oblivious">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">guard_size_oblivious</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#guard_size_oblivious"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.guard_size_oblivious" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">guard_size_oblivious</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#guard_size_oblivious"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.guard_size_oblivious" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a guard on a symbolic boolean expression in a size oblivious way.
 This is typically used when a non-oblivious test would result in a guard
 on a data dependent value of which we don’t know the value of at compile time.
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html b/main/generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html
index d0314d44943..f44713a4c9e 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.has_free_symbols<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-has-free-symbols" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.has_free_symbols">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">has_free_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#has_free_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.has_free_symbols" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">has_free_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#has_free_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.has_free_symbols" title="Permalink to this definition">¶</a></dt>
 <dd><p>Faster version of bool(free_symbols(val))</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols.html b/main/generated/torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols.html
index 609d26f1231..4559952a1c5 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-has-free-unbacked-symbols" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">has_free_unbacked_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#has_free_unbacked_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">has_free_unbacked_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#has_free_unbacked_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols" title="Permalink to this definition">¶</a></dt>
 <dd><p>Faster version of bool(free_unbacked_symbols(val))</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.hint_int.html b/main/generated/torch.fx.experimental.symbolic_shapes.hint_int.html
index 5d381984122..8bb7a8df805 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.hint_int.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.hint_int.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.hint_int<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-hint-int" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.hint_int">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">hint_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#hint_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.hint_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">hint_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#hint_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.hint_int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Retrieve the hint for an int (based on the underlying real values as observed
 at runtime).  If no hint is available (e.g., because data dependent shapes),
 if fallback is not None, use that instead (otherwise raise an error).</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.is_accessor_node.html b/main/generated/torch.fx.experimental.symbolic_shapes.is_accessor_node.html
index 8ce8e397735..930ec3138ae 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.is_accessor_node.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.is_accessor_node.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.is_accessor_node<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-is-accessor-node" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.is_accessor_node">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_accessor_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_accessor_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L587"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_accessor_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_accessor_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_accessor_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L587"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_accessor_node" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html
index 2d3f0ac3b4b..67d752b2b8f 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.is_concrete_bool<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-is-concrete-bool" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.is_concrete_bool">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L764"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L764"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_bool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to check if underlying object
 in SymBool is concrete value. Also returns
 true if integer is passed in.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_float.html b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_float.html
index 9de13d6166d..cd1429b8f9e 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_float.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_float.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.is_concrete_float<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-is-concrete-float" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.is_concrete_float">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to check if underlying object
 in SymInt is concrete value. Also returns
 true if integer is passed in.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html
index 2a7d0ab5474..0821580aef4 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.is_concrete_int<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-is-concrete-int" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.is_concrete_int">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to check if underlying object
 in SymInt is concrete value. Also returns
 true if integer is passed in.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.lru_cache.html b/main/generated/torch.fx.experimental.symbolic_shapes.lru_cache.html
index ee0676c1e42..8cdc6383fef 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.lru_cache.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.lru_cache.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.lru_cache<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-lru-cache" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.lru_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">lru_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">maxsize</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#lru_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.lru_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">lru_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">maxsize</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#lru_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.lru_cache" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>Callable[[Callable[…, _T]], functools._lru_cache_wrapper[_T]]</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.rebind_unbacked.html b/main/generated/torch.fx.experimental.symbolic_shapes.rebind_unbacked.html
index 59778da4417..971856a3ced 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.rebind_unbacked.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.rebind_unbacked.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.rebind_unbacked<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-rebind-unbacked" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.rebind_unbacked">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">rebind_unbacked</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#rebind_unbacked"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L466"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.rebind_unbacked" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">rebind_unbacked</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#rebind_unbacked"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L466"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.rebind_unbacked" title="Permalink to this definition">¶</a></dt>
 <dd><p>Suppose we are retracing a pre-existing FX graph that previously had
 fake tensor propagation (and therefore unbacked SymInts).  When we retrace,
 we re-propagate fake tensors, which results in new unbacked SymInts.
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings.html b/main/generated/torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings.html
index 99a94498eb5..d6362d37f4e 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-resolve-unbacked-bindings" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">resolve_unbacked_bindings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bindings</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#resolve_unbacked_bindings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">resolve_unbacked_bindings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bindings</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#resolve_unbacked_bindings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[sympy.core.symbol.Symbol, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)">tuple</a>[torch.utils._pytree.KeyEntry, …]]]</p>
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index bfbf9b5a743..01ddd3b9bb5 100644
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 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.statically_known_true">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">statically_known_true</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#statically_known_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.statically_known_true" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">statically_known_true</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#statically_known_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.statically_known_true" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if x can be simplified to a constant and is true.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.sym_eq.html b/main/generated/torch.fx.experimental.symbolic_shapes.sym_eq.html
index b3840059ee6..ebc918ec706 100644
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+++ b/main/generated/torch.fx.experimental.symbolic_shapes.sym_eq.html
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 <h1>torch.fx.experimental.symbolic_shapes.sym_eq<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-sym-eq" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.sym_eq">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">sym_eq</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#sym_eq"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/fx/experimental/symbolic_shapes.py#L1211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.sym_eq" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">sym_eq</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#sym_eq"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.sym_eq" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like ==, but when run on list/tuple, it will recursively test equality
 and use sym_and to join the results together, without guarding.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.gather.html b/main/generated/torch.gather.html
index cdb0e4bf903..d9b80b1353e 100644
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index 08bb1949cad..a8d62e25605 100644
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@@ -612,7 +612,7 @@
 <h1>torch.get_default_device<a class="headerlink" href="#torch-get-default-device" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.get_default_device">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_default_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_default_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_default_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_default_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_default_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_default_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gets the default <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> to be allocated on <code class="docutils literal notranslate"><span class="pre">device</span></code></p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.get_default_dtype.html b/main/generated/torch.get_default_dtype.html
index bbc4b02a443..17c7d8acb09 100644
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@@ -612,7 +612,7 @@
 <h1>torch.get_deterministic_debug_mode<a class="headerlink" href="#torch-get-deterministic-debug-mode" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.get_deterministic_debug_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_deterministic_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_deterministic_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_deterministic_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_deterministic_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_deterministic_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_deterministic_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current value of the debug mode for deterministic
 operations. Refer to <a class="reference internal" href="torch.set_deterministic_debug_mode.html#torch.set_deterministic_debug_mode" title="torch.set_deterministic_debug_mode"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_deterministic_debug_mode()</span></code></a>
 documentation for more details.</p>
diff --git a/main/generated/torch.get_device_module.html b/main/generated/torch.get_device_module.html
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@@ -612,7 +612,7 @@
 <h1>torch.get_device_module<a class="headerlink" href="#torch-get-device-module" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.get_device_module">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_device_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_device_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L2683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_device_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_device_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_device_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L2683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_device_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the module associated with a given device(e.g., torch.device(‘cuda’), “mtia:0”, “xpu”, …).
 If no device is given, return the module for the current accelerator or CPU if none is present.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.get_float32_matmul_precision<a class="headerlink" href="#torch-get-float32-matmul-precision" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.get_float32_matmul_precision">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_float32_matmul_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_float32_matmul_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_float32_matmul_precision" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_float32_matmul_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_float32_matmul_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_float32_matmul_precision" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current value of float32 matrix multiplication precision. Refer to
 <a class="reference internal" href="torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision" title="torch.set_float32_matmul_precision"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_float32_matmul_precision()</span></code></a> documentation for more details.</p>
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 <h1>torch.get_rng_state<a class="headerlink" href="#torch-get-rng-state" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the random number generator state as a <cite>torch.ByteTensor</cite>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
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@@ -612,7 +612,7 @@
 <h1>torch.initial_seed<a class="headerlink" href="#torch-initial-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.initial_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.initial_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.initial_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the initial seed for generating random numbers as a
 Python <cite>long</cite>.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.inner.html b/main/generated/torch.inner.html
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diff --git a/main/generated/torch.is_complex.html b/main/generated/torch.is_complex.html
index ef3f9da70b0..de6ec708278 100644
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diff --git a/main/generated/torch.is_conj.html b/main/generated/torch.is_conj.html
index 1cb7148cd71..440d9d08cf7 100644
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diff --git a/main/generated/torch.is_deterministic_algorithms_warn_only_enabled.html b/main/generated/torch.is_deterministic_algorithms_warn_only_enabled.html
index 820b9b13b61..e79a8958c21 100644
--- a/main/generated/torch.is_deterministic_algorithms_warn_only_enabled.html
+++ b/main/generated/torch.is_deterministic_algorithms_warn_only_enabled.html
@@ -300,7 +300,7 @@
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 <h1>torch.is_deterministic_algorithms_warn_only_enabled<a class="headerlink" href="#torch-is-deterministic-algorithms-warn-only-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.is_deterministic_algorithms_warn_only_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_deterministic_algorithms_warn_only_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_deterministic_algorithms_warn_only_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_deterministic_algorithms_warn_only_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_deterministic_algorithms_warn_only_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_deterministic_algorithms_warn_only_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_deterministic_algorithms_warn_only_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if the global deterministic flag is set to warn only.
 Refer to <a class="reference internal" href="torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a> documentation for more
 details.</p>
diff --git a/main/generated/torch.is_floating_point.html b/main/generated/torch.is_floating_point.html
index 4f5ff047e9a..f405052ed8e 100644
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index 1407a5ce607..29889a3b505 100644
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index 7de92a60073..80dfc7c3ebe 100644
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diff --git a/main/generated/torch.is_storage.html b/main/generated/torch.is_storage.html
index 5acbfb60bfc..e260f519e0b 100644
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 <h1>torch.is_storage<a class="headerlink" href="#torch-is-storage" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.is_storage">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_storage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_storage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if <cite>obj</cite> is a PyTorch storage object.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.is_tensor.html b/main/generated/torch.is_tensor.html
index 0628b422865..c50e44cf537 100644
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 <h1>torch.is_tensor<a class="headerlink" href="#torch-is-tensor" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.is_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1092"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1092"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if <cite>obj</cite> is a PyTorch tensor.</p>
 <p>Note that this function is simply doing <code class="docutils literal notranslate"><span class="pre">isinstance(obj,</span> <span class="pre">Tensor)</span></code>.
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diff --git a/main/generated/torch.is_warn_always_enabled.html b/main/generated/torch.is_warn_always_enabled.html
index 1c3f7590cea..97b7601848f 100644
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 <h1>torch.is_warn_always_enabled<a class="headerlink" href="#torch-is-warn-always-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.is_warn_always_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_warn_always_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_warn_always_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_warn_always_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_warn_always_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_warn_always_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_warn_always_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if the global warn_always flag is turned on. Refer to
 <a class="reference internal" href="torch.set_warn_always.html#torch.set_warn_always" title="torch.set_warn_always"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_warn_always()</span></code></a> documentation for more details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.isclose.html b/main/generated/torch.isclose.html
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index c7e86f20c2a..5afe942649d 100644
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index c0e416d8097..62955757dd4 100644
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diff --git a/main/generated/torch.isposinf.html b/main/generated/torch.isposinf.html
index 96045ac7c9c..800e7d5a3b2 100644
--- a/main/generated/torch.isposinf.html
+++ b/main/generated/torch.isposinf.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.isreal.html b/main/generated/torch.isreal.html
index 6974830462e..af662339f41 100644
--- a/main/generated/torch.isreal.html
+++ b/main/generated/torch.isreal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.istft.html b/main/generated/torch.istft.html
index 3cca43dc1a6..ed3f655313d 100644
--- a/main/generated/torch.istft.html
+++ b/main/generated/torch.istft.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.jit.Attribute.html b/main/generated/torch.jit.Attribute.html
index 8a29dc8c2b9..1c594029828 100644
--- a/main/generated/torch.jit.Attribute.html
+++ b/main/generated/torch.jit.Attribute.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.jit.ScriptFunction.html b/main/generated/torch.jit.ScriptFunction.html
index 72fac101b8c..aa6ac3383ef 100644
--- a/main/generated/torch.jit.ScriptFunction.html
+++ b/main/generated/torch.jit.ScriptFunction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.jit.ScriptModule.html b/main/generated/torch.jit.ScriptModule.html
index ed5b50f137a..0a7fc5ae775 100644
--- a/main/generated/torch.jit.ScriptModule.html
+++ b/main/generated/torch.jit.ScriptModule.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">ScriptModule</span></span><a class="reference internal" href="../_modules/torch/jit/_script.html#ScriptModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_script.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">ScriptModule</span></span><a class="reference internal" href="../_modules/torch/jit/_script.html#ScriptModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_script.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper for C++ torch::jit::Module with methods, attributes, and parameters.</p>
 <p>A wrapper around C++ <code class="docutils literal notranslate"><span class="pre">torch::jit::Module</span></code>. <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code>s
 contain methods, attributes, parameters, and
@@ -621,7 +621,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.add_module">
-<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.add_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.add_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a child module to the current module.</p>
 <p>The module can be accessed as an attribute using the given name.</p>
 <dl class="field-list simple">
@@ -637,7 +637,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p>
 <p>Typical use includes initializing the parameters of a model
 (see also <a class="reference internal" href="../nn.init.html#nn-init-doc"><span class="std std-ref">torch.nn.init</span></a>).</p>
@@ -679,7 +679,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -697,7 +697,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.buffers">
-<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -723,7 +723,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.children">
-<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -756,7 +756,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.compile">
-<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.compile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compile this Module’s forward using <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
 <p>This Module’s <cite>__call__</cite> method is compiled and all arguments are passed as-is
 to <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
@@ -765,7 +765,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the CPU.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -783,7 +783,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the GPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -808,7 +808,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -826,7 +826,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.eval">
-<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in evaluation mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -847,7 +847,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the extra representation of the module.</p>
 <p>To print customized extra information, you should re-implement
 this method in your own modules. Both single-line and multi-line
@@ -861,7 +861,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -879,7 +879,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.get_buffer">
-<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -906,7 +906,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.get_extra_state">
-<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return any extra state to include in the module’s state_dict.</p>
 <p>Implement this and a corresponding <a class="reference internal" href="#torch.jit.ScriptModule.set_extra_state" title="torch.jit.ScriptModule.set_extra_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_extra_state()</span></code></a> for your module
 if you need to store extra state. This function is called when building the
@@ -927,7 +927,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.get_parameter">
-<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -954,7 +954,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.get_submodule">
-<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>For example, let’s say you have an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> <code class="docutils literal notranslate"><span class="pre">A</span></code> that
 looks like this:</p>
@@ -1010,7 +1010,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1036,7 +1036,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.ipu">
-<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.ipu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.ipu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the IPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1061,7 +1061,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy parameters and buffers from <a class="reference internal" href="#torch.jit.ScriptModule.state_dict" title="torch.jit.ScriptModule.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> into this module and its descendants.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">strict</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then
 the keys of <a class="reference internal" href="#torch.jit.ScriptModule.state_dict" title="torch.jit.ScriptModule.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> must exactly match the keys returned
@@ -1116,7 +1116,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.modules">
-<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1148,7 +1148,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.mtia">
-<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.mtia" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.mtia" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the MTIA.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1173,7 +1173,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1202,7 +1202,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.named_children">
-<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1222,7 +1222,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.named_modules">
-<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1259,7 +1259,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1289,7 +1289,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.parameters">
-<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters.</p>
 <p>This is typically passed to an optimizer.</p>
 <dl class="field-list simple">
@@ -1316,7 +1316,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_backward_hook">
-<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>This function is deprecated in favor of <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.register_full_backward_hook" title="torch.nn.Module.register_full_backward_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">register_full_backward_hook()</span></code></a> and
 the behavior of this function will change in future versions.</p>
@@ -1333,7 +1333,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_buffer">
-<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a buffer to the module.</p>
 <p>This is typically used to register a buffer that should not to be
 considered a model parameter. For example, BatchNorm’s <code class="docutils literal notranslate"><span class="pre">running_mean</span></code>
@@ -1365,7 +1365,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_forward_hook">
-<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_forward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_forward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward hook on the module.</p>
 <p>The hook will be called every time after <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> has computed an output.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> or not specified, the input contains only
@@ -1416,7 +1416,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_forward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward pre-hook on the module.</p>
 <p>The hook will be called every time before <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> is invoked.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is false or not specified, the input contains only
@@ -1466,7 +1466,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_full_backward_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>The hook will be called every time the gradients with respect to a module
 are computed, i.e. the hook will execute if and only if the gradients with
@@ -1517,7 +1517,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_full_backward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook on the module.</p>
 <p>The hook will be called every time the gradients for the module are computed.
 The hook should have the following signature:</p>
@@ -1563,7 +1563,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook to be run after module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, incompatible_keys) -&gt; None</p>
@@ -1593,7 +1593,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook to be run before module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -&gt; None  # noqa: B950</p>
@@ -1609,7 +1609,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_module">
-<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Alias for <a class="reference internal" href="#torch.jit.ScriptModule.add_module" title="torch.jit.ScriptModule.add_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
@@ -1617,7 +1617,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_parameter">
-<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a parameter to the module.</p>
 <p>The parameter can be accessed as an attribute using given name.</p>
 <dl class="field-list simple">
@@ -1636,7 +1636,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook for the <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata) -&gt; None</p>
@@ -1647,7 +1647,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook for the <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, prefix, keep_vars) -&gt; None</p>
@@ -1659,7 +1659,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.requires_grad_">
-<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.requires_grad_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.requires_grad_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Change if autograd should record operations on parameters in this module.</p>
 <p>This method sets the parameters’ <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad</span></code> attributes
 in-place.</p>
@@ -1683,7 +1683,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.save">
-<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_script.py#L746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_script.py#L746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save with a file-like object.</p>
 <p>save(f, _extra_files={})</p>
 <p>See <a class="reference internal" href="torch.jit.save.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.save</span></code></a> which accepts a file-like object.
@@ -1693,7 +1693,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.set_extra_state">
-<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.set_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.set_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p>
 <p>This function is called from <a class="reference internal" href="#torch.jit.ScriptModule.load_state_dict" title="torch.jit.ScriptModule.load_state_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> to handle any extra state
 found within the <cite>state_dict</cite>. Implement this function and a corresponding
@@ -1708,7 +1708,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.set_submodule">
-<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.set_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.set_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1766,7 +1766,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.share_memory">
-<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.share_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.share_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.Tensor.share_memory_.html#torch.Tensor.share_memory_" title="torch.Tensor.share_memory_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1777,7 +1777,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary containing references to the whole state of the module.</p>
 <p>Both parameters and persistent buffers (e.g. running averages) are
 included. Keys are corresponding parameter and buffer names.
@@ -1830,27 +1830,27 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move and/or cast the parameters and buffers.</p>
 <p>This can be called as</p>
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <p>Its signature is similar to <a class="reference internal" href="torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.to()</span></code></a>, but only accepts
@@ -1929,7 +1929,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.to_empty">
-<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.to_empty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.to_empty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move the parameters and buffers to the specified device without copying storage.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1951,7 +1951,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.train">
-<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.train" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.train" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in training mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -1973,7 +1973,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1994,7 +1994,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.xpu">
-<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.xpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.xpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the XPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing optimizer if the module will
@@ -2019,7 +2019,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset gradients of all model parameters.</p>
 <p>See similar function under <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> for more context.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.jit.annotate.html b/main/generated/torch.jit.annotate.html
index 67a5af3dfdb..c93df25909b 100644
--- a/main/generated/torch.jit.annotate.html
+++ b/main/generated/torch.jit.annotate.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.annotate<a class="headerlink" href="#torch-jit-annotate" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.annotate">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">annotate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">the_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">the_value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#annotate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/__init__.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.annotate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">annotate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">the_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">the_value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#annotate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.annotate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Use to give type of <cite>the_value</cite> in TorchScript compiler.</p>
 <p>This method is a pass-through function that returns <cite>the_value</cite>, used to hint TorchScript
 compiler the type of <cite>the_value</cite>. It is a no-op when running outside of TorchScript.</p>
diff --git a/main/generated/torch.jit.enable_onednn_fusion.html b/main/generated/torch.jit.enable_onednn_fusion.html
index 9fba3c0dec5..7174a2d2eca 100644
--- a/main/generated/torch.jit.enable_onednn_fusion.html
+++ b/main/generated/torch.jit.enable_onednn_fusion.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.enable_onednn_fusion<a class="headerlink" href="#torch-jit-enable-onednn-fusion" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.enable_onednn_fusion">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">enable_onednn_fusion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#enable_onednn_fusion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/__init__.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.enable_onednn_fusion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">enable_onednn_fusion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#enable_onednn_fusion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.enable_onednn_fusion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable or disables onednn JIT fusion based on the parameter <cite>enabled</cite>.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.jit.fork.html b/main/generated/torch.jit.fork.html
index 6c49fb141b3..0992042968d 100644
--- a/main/generated/torch.jit.fork.html
+++ b/main/generated/torch.jit.fork.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.fork<a class="headerlink" href="#torch-jit-fork" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.fork">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">fork</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_async.html#fork"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_async.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.fork" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">fork</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_async.html#fork"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_async.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.fork" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create an asynchronous task executing <cite>func</cite> and a reference to the value of the result of this execution.</p>
 <p><cite>fork</cite> will return immediately, so the return value of <cite>func</cite> may not have been computed yet. To force completion
 of the task and access the return value invoke <cite>torch.jit.wait</cite> on the Future. <cite>fork</cite> invoked
diff --git a/main/generated/torch.jit.freeze.html b/main/generated/torch.jit.freeze.html
index 5aced7b3128..79d6a0b39c9 100644
--- a/main/generated/torch.jit.freeze.html
+++ b/main/generated/torch.jit.freeze.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.freeze<a class="headerlink" href="#torch-jit-freeze" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.freeze">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">freeze</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserved_attrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize_numerics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_freeze.html#freeze"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_freeze.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.freeze" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">freeze</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserved_attrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize_numerics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_freeze.html#freeze"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_freeze.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.freeze" title="Permalink to this definition">¶</a></dt>
 <dd><p>Freeze ScriptModule, inline submodules, and attributes as constants.</p>
 <p>Freezing a <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> will clone it and attempt to inline the cloned
 module’s submodules, parameters, and attributes as constants in the TorchScript IR Graph.
diff --git a/main/generated/torch.jit.ignore.html b/main/generated/torch.jit.ignore.html
index 24f0deaabf2..e1259d45917 100644
--- a/main/generated/torch.jit.ignore.html
+++ b/main/generated/torch.jit.ignore.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.ignore<a class="headerlink" href="#torch-jit-ignore" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.ignore">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">ignore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_jit_internal.html#ignore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L781"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ignore" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">ignore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_jit_internal.html#ignore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L781"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ignore" title="Permalink to this definition">¶</a></dt>
 <dd><p>This decorator indicates to the compiler that a function or method should
 be ignored and left as a Python function. This allows you to leave code in
 your model that is not yet TorchScript compatible. If called from TorchScript,
diff --git a/main/generated/torch.jit.interface.html b/main/generated/torch.jit.interface.html
index 4ba4b8342ca..8dac9fe63d2 100644
--- a/main/generated/torch.jit.interface.html
+++ b/main/generated/torch.jit.interface.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.interface<a class="headerlink" href="#torch-jit-interface" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.interface">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">interface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_script.html#interface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_script.py#L1530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.interface" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">interface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_script.html#interface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_script.py#L1530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.interface" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decorate to annotate classes or modules of different types.</p>
 <p>This decorator can be used to define an interface that can be used to annotate
 classes or modules of different types. This can be used for to annotate a submodule
diff --git a/main/generated/torch.jit.isinstance.html b/main/generated/torch.jit.isinstance.html
index 5eb89e521bb..67a201daabd 100644
--- a/main/generated/torch.jit.isinstance.html
+++ b/main/generated/torch.jit.isinstance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.isinstance<a class="headerlink" href="#torch-jit-isinstance" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.isinstance">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">isinstance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#isinstance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/__init__.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.isinstance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">isinstance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#isinstance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.isinstance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Provide container type refinement in TorchScript.</p>
 <p>It can refine parameterized containers of the List, Dict, Tuple, and Optional types. E.g. <code class="docutils literal notranslate"><span class="pre">List[str]</span></code>,
 <code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">List[torch.Tensor]]</span></code>, <code class="docutils literal notranslate"><span class="pre">Optional[Tuple[int,str,int]]</span></code>. It can also
diff --git a/main/generated/torch.jit.load.html b/main/generated/torch.jit.load.html
index 4f13f37751f..c7058663541 100644
--- a/main/generated/torch.jit.load.html
+++ b/main/generated/torch.jit.load.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.load<a class="headerlink" href="#torch-jit-load" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.load">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_restore_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_serialization.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_serialization.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_restore_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_serialization.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_serialization.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> or <a class="reference internal" href="torch.jit.ScriptFunction.html#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a> previously saved with <a class="reference internal" href="torch.jit.save.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.save</span></code></a>.</p>
 <p>All previously saved modules, no matter their device, are first loaded onto CPU,
 and then are moved to the devices they were saved from. If this fails (e.g.
diff --git a/main/generated/torch.jit.onednn_fusion_enabled.html b/main/generated/torch.jit.onednn_fusion_enabled.html
index 12bb1abdac3..86b34fff878 100644
--- a/main/generated/torch.jit.onednn_fusion_enabled.html
+++ b/main/generated/torch.jit.onednn_fusion_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.onednn_fusion_enabled<a class="headerlink" href="#torch-jit-onednn-fusion-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.onednn_fusion_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">onednn_fusion_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#onednn_fusion_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/__init__.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.onednn_fusion_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">onednn_fusion_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#onednn_fusion_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.onednn_fusion_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether onednn JIT fusion is enabled.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.jit.optimize_for_inference.html b/main/generated/torch.jit.optimize_for_inference.html
index a594e3c0fdd..da3b7ff58d9 100644
--- a/main/generated/torch.jit.optimize_for_inference.html
+++ b/main/generated/torch.jit.optimize_for_inference.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.optimize_for_inference<a class="headerlink" href="#torch-jit-optimize-for-inference" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.optimize_for_inference">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">optimize_for_inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_methods</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_freeze.html#optimize_for_inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_freeze.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.optimize_for_inference" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">optimize_for_inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_methods</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_freeze.html#optimize_for_inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_freeze.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.optimize_for_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a set of optimization passes to optimize a model for the purposes of inference.</p>
 <p>If the model is not already frozen, optimize_for_inference
 will invoke <cite>torch.jit.freeze</cite> automatically.</p>
diff --git a/main/generated/torch.jit.save.html b/main/generated/torch.jit.save.html
index 7893f3cccb1..09bca609116 100644
--- a/main/generated/torch.jit.save.html
+++ b/main/generated/torch.jit.save.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.save<a class="headerlink" href="#torch-jit-save" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.save">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_serialization.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_serialization.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_serialization.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_serialization.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save an offline version of this module for use in a separate process.</p>
 <p>The saved module serializes all of the methods, submodules, parameters, and
 attributes of this module. It can be loaded into the C++ API using
diff --git a/main/generated/torch.jit.script.html b/main/generated/torch.jit.script.html
index 5a42551e9b8..f75a2f9363f 100644
--- a/main/generated/torch.jit.script.html
+++ b/main/generated/torch.jit.script.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.script<a class="headerlink" href="#torch-jit-script" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.script">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">script</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_frames_up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_rcb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_script.html#script"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_script.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.script" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">script</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_frames_up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_rcb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_script.html#script"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_script.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.script" title="Permalink to this definition">¶</a></dt>
 <dd><p>Script the function.</p>
 <p>Scripting a function or <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> will inspect the source code, compile
 it as TorchScript code using the TorchScript compiler, and return a <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> or
diff --git a/main/generated/torch.jit.script_if_tracing.html b/main/generated/torch.jit.script_if_tracing.html
index 543987b4a63..e21e0e83945 100644
--- a/main/generated/torch.jit.script_if_tracing.html
+++ b/main/generated/torch.jit.script_if_tracing.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.script_if_tracing<a class="headerlink" href="#torch-jit-script-if-tracing" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.script_if_tracing">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">script_if_tracing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#script_if_tracing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/__init__.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.script_if_tracing" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">script_if_tracing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#script_if_tracing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.script_if_tracing" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compiles <code class="docutils literal notranslate"><span class="pre">fn</span></code> when it is first called during tracing.</p>
 <p><code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> has a non-negligible start up time when it is first called due to
 lazy-initializations of many compiler builtins. Therefore you should not use
diff --git a/main/generated/torch.jit.set_fusion_strategy.html b/main/generated/torch.jit.set_fusion_strategy.html
index 3b4fc69a10d..dbdf86def4a 100644
--- a/main/generated/torch.jit.set_fusion_strategy.html
+++ b/main/generated/torch.jit.set_fusion_strategy.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.set_fusion_strategy<a class="headerlink" href="#torch-jit-set-fusion-strategy" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.set_fusion_strategy">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">set_fusion_strategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">strategy</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_fuser.html#set_fusion_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_fuser.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.set_fusion_strategy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">set_fusion_strategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">strategy</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_fuser.html#set_fusion_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_fuser.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.set_fusion_strategy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the type and number of specializations that can occur during fusion.</p>
 <p>Usage: provide a list of pairs (type, depth) where type is one of “STATIC” or “DYNAMIC”
 and depth is an integer.</p>
diff --git a/main/generated/torch.jit.strict_fusion.html b/main/generated/torch.jit.strict_fusion.html
index 31aa1da65d5..5b965d0d57e 100644
--- a/main/generated/torch.jit.strict_fusion.html
+++ b/main/generated/torch.jit.strict_fusion.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>strict_fusion<a class="headerlink" href="#strict-fusion" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.jit.strict_fusion">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">strict_fusion</span></span><a class="reference internal" href="../_modules/torch/jit.html#strict_fusion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.strict_fusion" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">strict_fusion</span></span><a class="reference internal" href="../_modules/torch/jit.html#strict_fusion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.strict_fusion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Give errors if not all nodes have been fused in inference, or symbolically differentiated in training.</p>
 <p>Example:
 Forcing fusion of additions.</p>
diff --git a/main/generated/torch.jit.trace.html b/main/generated/torch.jit.trace.html
index ef174a39c4f..0edf21d14c0 100644
--- a/main/generated/torch.jit.trace.html
+++ b/main/generated/torch.jit.trace.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.trace<a class="headerlink" href="#torch-jit-trace" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.trace">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_trace=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_tolerance=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_force_outplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_class=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compilation_unit=&lt;torch.jit.CompilationUnit</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_kwarg_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_store_inputs=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_trace.html#trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_trace.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_trace=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_tolerance=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_force_outplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_class=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compilation_unit=&lt;torch.jit.CompilationUnit</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_kwarg_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_store_inputs=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_trace.html#trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_trace.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Trace a function and return an executable  or <a class="reference internal" href="torch.jit.ScriptFunction.html#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a> that will be optimized using just-in-time compilation.</p>
 <p>Tracing is ideal for code that operates only on
 <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>\s and lists, dictionaries, and
diff --git a/main/generated/torch.jit.trace_module.html b/main/generated/torch.jit.trace_module.html
index 4e12653fa80..1374a889161 100644
--- a/main/generated/torch.jit.trace_module.html
+++ b/main/generated/torch.jit.trace_module.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.trace_module<a class="headerlink" href="#torch-jit-trace-module" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.trace_module">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">trace_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_trace=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_tolerance=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_force_outplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_class=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compilation_unit=&lt;torch.jit.CompilationUnit</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs_is_kwarg=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_store_inputs=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_trace.html#trace_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_trace.py#L1120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.trace_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">trace_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_trace=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_tolerance=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_force_outplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_class=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compilation_unit=&lt;torch.jit.CompilationUnit</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs_is_kwarg=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_store_inputs=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_trace.html#trace_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_trace.py#L1120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.trace_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Trace a module and return an executable <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> that will be optimized using just-in-time compilation.</p>
 <p>When a module is passed to <a class="reference internal" href="torch.jit.trace.html#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace</span></code></a>, only
 the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method is run and traced. With <code class="docutils literal notranslate"><span class="pre">trace_module</span></code>, you can specify a dictionary of
diff --git a/main/generated/torch.jit.unused.html b/main/generated/torch.jit.unused.html
index fbeecc6a3f0..21e9784fa0c 100644
--- a/main/generated/torch.jit.unused.html
+++ b/main/generated/torch.jit.unused.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.unused<a class="headerlink" href="#torch-jit-unused" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.unused">
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+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">unused</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_jit_internal.html#unused"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L715"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.unused" title="Permalink to this definition">¶</a></dt>
 <dd><p>This decorator indicates to the compiler that a function or method should
 be ignored and replaced with the raising of an exception. This allows you
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+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">future</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_async.html#wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_async.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.wait" title="Permalink to this definition">¶</a></dt>
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diff --git a/main/generated/torch.load.html b/main/generated/torch.load.html
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@@ -612,7 +612,7 @@
 <h1>torch.load<a class="headerlink" href="#torch-load" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.load">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">pickle</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">pickle_load_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L1229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">pickle</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">pickle_load_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L1229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads an object saved with <a class="reference internal" href="torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> from a file.</p>
 <p><a class="reference internal" href="#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> uses Python’s unpickling facilities but treats storages,
 which underlie tensors, specially. They are first deserialized on the
diff --git a/main/generated/torch.lobpcg.html b/main/generated/torch.lobpcg.html
index 523b9921c62..52f0e85e7d7 100644
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+++ b/main/generated/torch.lobpcg.html
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@@ -612,7 +612,7 @@
 <h1>torch.lobpcg<a class="headerlink" href="#torch-lobpcg" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.lobpcg">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">lobpcg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iK</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">largest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracker</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_iparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_fparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_bparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lobpcg.html#lobpcg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_lobpcg.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.lobpcg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">lobpcg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iK</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">largest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracker</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_iparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_fparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_bparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lobpcg.html#lobpcg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_lobpcg.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.lobpcg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find the k largest (or smallest) eigenvalues and the corresponding
 eigenvectors of a symmetric positive definite generalized
 eigenvalue problem using matrix-free LOBPCG methods.</p>
diff --git a/main/generated/torch.log.html b/main/generated/torch.log.html
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@@ -612,7 +612,7 @@
 <h1>torch.lu<a class="headerlink" href="#torch-lu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.lu">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">lu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.lu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">lu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.lu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the LU factorization of a matrix or batches of matrices
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">A</span></code>. Returns a tuple containing the LU factorization and
 pivots of <code class="xref py py-attr docutils literal notranslate"><span class="pre">A</span></code>.  Pivoting is done if <code class="xref py py-attr docutils literal notranslate"><span class="pre">pivot</span></code> is set to
diff --git a/main/generated/torch.lu_solve.html b/main/generated/torch.lu_solve.html
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diff --git a/main/generated/torch.manual_seed.html b/main/generated/torch.manual_seed.html
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@@ -612,7 +612,7 @@
 <h1>torch.manual_seed<a class="headerlink" href="#torch-manual-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers on all devices. Returns a
 <cite>torch.Generator</cite> object.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.masked_select.html b/main/generated/torch.masked_select.html
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diff --git a/main/generated/torch.meshgrid.html b/main/generated/torch.meshgrid.html
index 3195e8cfeef..b61b0f4d848 100644
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@@ -612,7 +612,7 @@
 <h1>torch.meshgrid<a class="headerlink" href="#torch-meshgrid" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.meshgrid">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">meshgrid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indexing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#meshgrid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.meshgrid" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">meshgrid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indexing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#meshgrid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.meshgrid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates grids of coordinates specified by the 1D inputs in <cite>attr</cite>:tensors.</p>
 <p>This is helpful when you want to visualize data over some
 range of inputs. See below for a plotting example.</p>
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diff --git a/main/generated/torch.mps.current_allocated_memory.html b/main/generated/torch.mps.current_allocated_memory.html
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@@ -612,7 +612,7 @@
 <h1>torch.mps.current_allocated_memory<a class="headerlink" href="#torch-mps-current-allocated-memory" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.current_allocated_memory">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">current_allocated_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#current_allocated_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.current_allocated_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">current_allocated_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#current_allocated_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.current_allocated_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current GPU memory occupied by tensors in bytes.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.mps.device_count.html b/main/generated/torch.mps.device_count.html
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 <h1>torch.mps.device_count<a class="headerlink" href="#torch-mps-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the number of available MPS devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mps.driver_allocated_memory.html b/main/generated/torch.mps.driver_allocated_memory.html
index e2f12a06200..c8f456fff1d 100644
--- a/main/generated/torch.mps.driver_allocated_memory.html
+++ b/main/generated/torch.mps.driver_allocated_memory.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.driver_allocated_memory<a class="headerlink" href="#torch-mps-driver-allocated-memory" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.driver_allocated_memory">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">driver_allocated_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#driver_allocated_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.driver_allocated_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">driver_allocated_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#driver_allocated_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.driver_allocated_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns total GPU memory allocated by Metal driver for the process in bytes.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.mps.empty_cache.html b/main/generated/torch.mps.empty_cache.html
index a0c96a3111a..a7519e46a35 100644
--- a/main/generated/torch.mps.empty_cache.html
+++ b/main/generated/torch.mps.empty_cache.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.empty_cache<a class="headerlink" href="#torch-mps-empty-cache" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.empty_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.empty_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.empty_cache" title="Permalink to this definition">¶</a></dt>
 <dd><p>Releases all unoccupied cached memory currently held by the caching
 allocator so that those can be used in other GPU applications.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.mps.event.Event.html b/main/generated/torch.mps.event.Event.html
index 243fbf6126f..24c4691e233 100644
--- a/main/generated/torch.mps.event.Event.html
+++ b/main/generated/torch.mps.event.Event.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.mps.event.Event">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mps.event.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/event.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mps.event.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around an MPS event.</p>
 <p>MPS events are synchronization markers that can be used to monitor the
 device’s progress, to accurately measure timing, and to synchronize MPS streams.</p>
@@ -624,7 +624,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.elapsed_time">
-<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/event.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the time elapsed in milliseconds after the event was
 recorded and before the end_event was recorded.</p>
 <dl class="field-list simple">
@@ -636,7 +636,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/event.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if all work currently captured by event has completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -647,7 +647,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.record">
-<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/event.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.record" title="Permalink to this definition">¶</a></dt>
 <dd><p>Records the event in the default stream.</p>
 <dl class="field-list simple">
 </dl>
@@ -655,7 +655,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/event.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits until the completion of all work currently captured in this event.
 This prevents the CPU thread from proceeding until the event completes.</p>
 <dl class="field-list simple">
@@ -664,7 +664,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/event.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Makes all future work submitted to the default stream wait for this event.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.mps.get_rng_state.html b/main/generated/torch.mps.get_rng_state.html
index 71f82ce087a..ec63ce8f467 100644
--- a/main/generated/torch.mps.get_rng_state.html
+++ b/main/generated/torch.mps.get_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.get_rng_state<a class="headerlink" href="#torch-mps-get-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mps'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mps'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the random number generator state as a ByteTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mps.manual_seed.html b/main/generated/torch.mps.manual_seed.html
index 964cb507101..e238d5e45b9 100644
--- a/main/generated/torch.mps.manual_seed.html
+++ b/main/generated/torch.mps.manual_seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.manual_seed<a class="headerlink" href="#torch-mps-manual-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mps.profiler.is_capturing_metal.html b/main/generated/torch.mps.profiler.is_capturing_metal.html
index b6931cabf7e..94c90d5d75d 100644
--- a/main/generated/torch.mps.profiler.is_capturing_metal.html
+++ b/main/generated/torch.mps.profiler.is_capturing_metal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.is_capturing_metal<a class="headerlink" href="#torch-mps-profiler-is-capturing-metal" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.is_capturing_metal">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">is_capturing_metal</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#is_capturing_metal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/profiler.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.is_capturing_metal" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">is_capturing_metal</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#is_capturing_metal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.is_capturing_metal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Cheks if metal capture is in progress</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mps.profiler.is_metal_capture_enabled.html b/main/generated/torch.mps.profiler.is_metal_capture_enabled.html
index 60247aca13f..04afa894d59 100644
--- a/main/generated/torch.mps.profiler.is_metal_capture_enabled.html
+++ b/main/generated/torch.mps.profiler.is_metal_capture_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.is_metal_capture_enabled<a class="headerlink" href="#torch-mps-profiler-is-metal-capture-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.is_metal_capture_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">is_metal_capture_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#is_metal_capture_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/profiler.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.is_metal_capture_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">is_metal_capture_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#is_metal_capture_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.is_metal_capture_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checks if <cite>metal_capture</cite> context manager is usable
 To enable metal capture, set MTL_CAPTURE_ENABLED envvar</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.mps.profiler.metal_capture.html b/main/generated/torch.mps.profiler.metal_capture.html
index e51ba3569fe..2fdc5f8960a 100644
--- a/main/generated/torch.mps.profiler.metal_capture.html
+++ b/main/generated/torch.mps.profiler.metal_capture.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.metal_capture<a class="headerlink" href="#torch-mps-profiler-metal-capture" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.metal_capture">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">metal_capture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#metal_capture"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/profiler.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.metal_capture" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">metal_capture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#metal_capture"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.metal_capture" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conext manager that enables capturing of Metal calls into gputrace</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.mps.profiler.profile.html b/main/generated/torch.mps.profiler.profile.html
index 1d315d6e143..6b7b58ae8fc 100644
--- a/main/generated/torch.mps.profiler.profile.html
+++ b/main/generated/torch.mps.profiler.profile.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.profile<a class="headerlink" href="#torch-mps-profiler-profile" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.profile">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'interval'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_until_completed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/profiler.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.profile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'interval'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_until_completed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.profile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context Manager to enabling generating OS Signpost tracing from MPS backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mps.profiler.start.html b/main/generated/torch.mps.profiler.start.html
index a99a2691613..f7ea816bf83 100644
--- a/main/generated/torch.mps.profiler.start.html
+++ b/main/generated/torch.mps.profiler.start.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.start<a class="headerlink" href="#torch-mps-profiler-start" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.start">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'interval'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_until_completed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/profiler.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.start" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'interval'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_until_completed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.start" title="Permalink to this definition">¶</a></dt>
 <dd><p>Start OS Signpost tracing from MPS backend.</p>
 <p>The generated OS Signposts could be recorded and viewed in
 XCode Instruments Logging tool.</p>
diff --git a/main/generated/torch.mps.profiler.stop.html b/main/generated/torch.mps.profiler.stop.html
index 847c8857cbc..6898799f507 100644
--- a/main/generated/torch.mps.profiler.stop.html
+++ b/main/generated/torch.mps.profiler.stop.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.stop<a class="headerlink" href="#torch-mps-profiler-stop" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.stop">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/profiler.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.stop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.stop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Stops generating OS Signpost tracing from MPS backend.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.mps.recommended_max_memory.html b/main/generated/torch.mps.recommended_max_memory.html
index e6713fdd0fe..0d0c321b69c 100644
--- a/main/generated/torch.mps.recommended_max_memory.html
+++ b/main/generated/torch.mps.recommended_max_memory.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.recommended_max_memory<a class="headerlink" href="#torch-mps-recommended-max-memory" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.recommended_max_memory">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">recommended_max_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#recommended_max_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.recommended_max_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">recommended_max_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#recommended_max_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.recommended_max_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns recommended max Working set size for GPU memory in bytes.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.mps.seed.html b/main/generated/torch.mps.seed.html
index 605a47261b6..1a683b3aa1d 100644
--- a/main/generated/torch.mps.seed.html
+++ b/main/generated/torch.mps.seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.seed<a class="headerlink" href="#torch-mps-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers to a random number.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.mps.set_per_process_memory_fraction.html b/main/generated/torch.mps.set_per_process_memory_fraction.html
index a191cb3da97..4e12d4fc2dc 100644
--- a/main/generated/torch.mps.set_per_process_memory_fraction.html
+++ b/main/generated/torch.mps.set_per_process_memory_fraction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.set_per_process_memory_fraction<a class="headerlink" href="#torch-mps-set-per-process-memory-fraction" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.set_per_process_memory_fraction">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">set_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fraction</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#set_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.set_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">set_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fraction</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#set_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.set_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set memory fraction for limiting process’s memory allocation on MPS device.
 The allowed value equals the fraction multiplied by recommended maximum device memory
 (obtained from Metal API device.recommendedMaxWorkingSetSize).
diff --git a/main/generated/torch.mps.set_rng_state.html b/main/generated/torch.mps.set_rng_state.html
index 87327e6b3c4..403f0187949 100644
--- a/main/generated/torch.mps.set_rng_state.html
+++ b/main/generated/torch.mps.set_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.set_rng_state<a class="headerlink" href="#torch-mps-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mps'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mps'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the random number generator state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mps.synchronize.html b/main/generated/torch.mps.synchronize.html
index 43b00ded3e3..62c4eefde4f 100644
--- a/main/generated/torch.mps.synchronize.html
+++ b/main/generated/torch.mps.synchronize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.synchronize<a class="headerlink" href="#torch-mps-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mps/__init__.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits for all kernels in all streams on a MPS device to complete.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.msort.html b/main/generated/torch.msort.html
index feb7ab6bc9b..09bc3bfe16c 100644
--- a/main/generated/torch.msort.html
+++ b/main/generated/torch.msort.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.mtia.DeferredMtiaCallError.html b/main/generated/torch.mtia.DeferredMtiaCallError.html
index c4a35c340bd..14eeab5ca64 100644
--- a/main/generated/torch.mtia.DeferredMtiaCallError.html
+++ b/main/generated/torch.mtia.DeferredMtiaCallError.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.DeferredMtiaCallError<a class="headerlink" href="#torch-mtia-deferredmtiacallerror" title="Permalink to this heading">¶</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="torch.mtia.DeferredMtiaCallError">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">DeferredMtiaCallError</span></span><a class="reference internal" href="../_modules/torch/mtia.html#DeferredMtiaCallError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.DeferredMtiaCallError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">DeferredMtiaCallError</span></span><a class="reference internal" href="../_modules/torch/mtia.html#DeferredMtiaCallError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.DeferredMtiaCallError" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.mtia.Event.html b/main/generated/torch.mtia.Event.html
index 9db7ee799ac..1bc3551ea65 100644
--- a/main/generated/torch.mtia.Event.html
+++ b/main/generated/torch.mtia.Event.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.mtia.Stream.html b/main/generated/torch.mtia.Stream.html
index 9e24e8a705d..0fbcb9d9dff 100644
--- a/main/generated/torch.mtia.Stream.html
+++ b/main/generated/torch.mtia.Stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.mtia.StreamContext.html b/main/generated/torch.mtia.StreamContext.html
index 3f18409d98a..805ca7c0609 100644
--- a/main/generated/torch.mtia.StreamContext.html
+++ b/main/generated/torch.mtia.StreamContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StreamContext<a class="headerlink" href="#streamcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.mtia.StreamContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.StreamContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.StreamContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that selects a given stream.</p>
 <p>All MTIA kernels queued within its context will be enqueued on a selected
 stream.</p>
diff --git a/main/generated/torch.mtia.current_device.html b/main/generated/torch.mtia.current_device.html
index d1faa458692..e78024d2fae 100644
--- a/main/generated/torch.mtia.current_device.html
+++ b/main/generated/torch.mtia.current_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.current_device<a class="headerlink" href="#torch-mtia-current-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.current_device">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.current_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.current_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mtia.current_stream.html b/main/generated/torch.mtia.current_stream.html
index 233031731c6..4f9232f13df 100644
--- a/main/generated/torch.mtia.current_stream.html
+++ b/main/generated/torch.mtia.current_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.current_stream<a class="headerlink" href="#torch-mtia-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the currently selected <a class="reference internal" href="torch.mtia.Stream.html#torch.mtia.Stream" title="torch.mtia.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.default_stream.html b/main/generated/torch.mtia.default_stream.html
index 4f3288160a0..f908789ba6f 100644
--- a/main/generated/torch.mtia.default_stream.html
+++ b/main/generated/torch.mtia.default_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.default_stream<a class="headerlink" href="#torch-mtia-default-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.default_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">default_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#default_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.default_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">default_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#default_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.default_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the default <a class="reference internal" href="torch.mtia.Stream.html#torch.mtia.Stream" title="torch.mtia.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.device.html b/main/generated/torch.mtia.device.html
index 595d43b6c55..ac77ffad7e2 100644
--- a/main/generated/torch.mtia.device.html
+++ b/main/generated/torch.mtia.device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>device<a class="headerlink" href="#device" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.mtia.device">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L222"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.device" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L222"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.device_count.html b/main/generated/torch.mtia.device_count.html
index ca5c088a201..be229671ec8 100644
--- a/main/generated/torch.mtia.device_count.html
+++ b/main/generated/torch.mtia.device_count.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.device_count<a class="headerlink" href="#torch-mtia-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of MTIA devices available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mtia.empty_cache.html b/main/generated/torch.mtia.empty_cache.html
index da3fa0be775..f9173f525b3 100644
--- a/main/generated/torch.mtia.empty_cache.html
+++ b/main/generated/torch.mtia.empty_cache.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.empty_cache<a class="headerlink" href="#torch-mtia-empty-cache" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.empty_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.empty_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.empty_cache" title="Permalink to this definition">¶</a></dt>
 <dd><p>Empty the MTIA device cache.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.mtia.get_device_capability.html b/main/generated/torch.mtia.get_device_capability.html
index adb5d517c0b..ab9bed93c90 100644
--- a/main/generated/torch.mtia.get_device_capability.html
+++ b/main/generated/torch.mtia.get_device_capability.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.get_device_capability<a class="headerlink" href="#torch-mtia-get-device-capability" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.get_device_capability">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.get_device_capability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.get_device_capability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return capability of a given device as a tuple of (major version, minor version).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.get_rng_state.html b/main/generated/torch.mtia.get_rng_state.html
index e0e35666b8c..a07c8672b1b 100644
--- a/main/generated/torch.mtia.get_rng_state.html
+++ b/main/generated/torch.mtia.get_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.get_rng_state<a class="headerlink" href="#torch-mtia-get-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mtia'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mtia'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the random number generator state as a ByteTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.init.html b/main/generated/torch.mtia.init.html
index f7ddcd904c7..2c7e3a15aae 100644
--- a/main/generated/torch.mtia.init.html
+++ b/main/generated/torch.mtia.init.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.init<a class="headerlink" href="#torch-mtia-init" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.init">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.init" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.init" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.mtia.is_available.html b/main/generated/torch.mtia.is_available.html
index fb6e39fc23c..7eefabd6b19 100644
--- a/main/generated/torch.mtia.is_available.html
+++ b/main/generated/torch.mtia.is_available.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.is_available<a class="headerlink" href="#torch-mtia-is-available" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return true if MTIA device is available</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mtia.is_initialized.html b/main/generated/torch.mtia.is_initialized.html
index de04bf2945f..a6a82136c63 100644
--- a/main/generated/torch.mtia.is_initialized.html
+++ b/main/generated/torch.mtia.is_initialized.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.is_initialized<a class="headerlink" href="#torch-mtia-is-initialized" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.is_initialized">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.is_initialized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.is_initialized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch’s MTIA state has been initialized.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.mtia.memory.memory_stats.html b/main/generated/torch.mtia.memory.memory_stats.html
index f13db303e66..a4f50dc76ee 100644
--- a/main/generated/torch.mtia.memory.memory_stats.html
+++ b/main/generated/torch.mtia.memory.memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.memory.memory_stats<a class="headerlink" href="#torch-mtia-memory-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.memory.memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.memory.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.memory.memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.memory.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.memory.memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of MTIA memory allocator statistics for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.memory_stats.html b/main/generated/torch.mtia.memory_stats.html
index 844d90e11c9..8e641b61163 100644
--- a/main/generated/torch.mtia.memory_stats.html
+++ b/main/generated/torch.mtia.memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.memory_stats<a class="headerlink" href="#torch-mtia-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of MTIA memory allocator statistics for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.record_memory_history.html b/main/generated/torch.mtia.record_memory_history.html
index 5b1072df00f..c61fbf3a49a 100644
--- a/main/generated/torch.mtia.record_memory_history.html
+++ b/main/generated/torch.mtia.record_memory_history.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.record_memory_history<a class="headerlink" href="#torch-mtia-record-memory-history" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.record_memory_history">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">record_memory_history</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'python'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_entries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#record_memory_history"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.record_memory_history" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">record_memory_history</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'python'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_entries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#record_memory_history"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.record_memory_history" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable/Disable the memory profiler on MTIA allocator</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.set_device.html b/main/generated/torch.mtia.set_device.html
index 7761e825916..1099fdd5d24 100644
--- a/main/generated/torch.mtia.set_device.html
+++ b/main/generated/torch.mtia.set_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.set_device<a class="headerlink" href="#torch-mtia-set-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.set_device">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.set_rng_state.html b/main/generated/torch.mtia.set_rng_state.html
index 297b7d537ae..624ec796f3b 100644
--- a/main/generated/torch.mtia.set_rng_state.html
+++ b/main/generated/torch.mtia.set_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.set_rng_state<a class="headerlink" href="#torch-mtia-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mtia'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L326"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mtia'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L326"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the random number generator state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.set_stream.html b/main/generated/torch.mtia.set_stream.html
index 8754d36d867..1520a346514 100644
--- a/main/generated/torch.mtia.set_stream.html
+++ b/main/generated/torch.mtia.set_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.set_stream<a class="headerlink" href="#torch-mtia-set-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.set_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_stream" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Set the current stream.This is a wrapper API to set the stream.</dt><dd><p>Usage of this function is discouraged in favor of the <code class="docutils literal notranslate"><span class="pre">stream</span></code>
 context manager.</p>
diff --git a/main/generated/torch.mtia.snapshot.html b/main/generated/torch.mtia.snapshot.html
index 3d59c54456c..9fb6ff4b2ee 100644
--- a/main/generated/torch.mtia.snapshot.html
+++ b/main/generated/torch.mtia.snapshot.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.snapshot<a class="headerlink" href="#torch-mtia-snapshot" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.snapshot">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of MTIA memory allocator history</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mtia.stream.html b/main/generated/torch.mtia.stream.html
index 81fbb6369ef..5c81aa1831a 100644
--- a/main/generated/torch.mtia.stream.html
+++ b/main/generated/torch.mtia.stream.html
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@@ -612,7 +612,7 @@
 <h1>torch.mtia.stream<a class="headerlink" href="#torch-mtia-stream" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap around the Context-manager StreamContext that selects a given stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.synchronize.html b/main/generated/torch.mtia.synchronize.html
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 <h1>torch.mtia.synchronize<a class="headerlink" href="#torch-mtia-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/mtia/__init__.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits for all jobs in all streams on a MTIA device to complete.</p>
 <dl class="field-list simple">
 </dl>
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index 2b7bdff119e..0b1e03fccb3 100644
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index 65f350b63e2..f6cc17a473b 100644
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@@ -612,7 +612,7 @@
 <h1>AdaptiveAvgPool1d<a class="headerlink" href="#adaptiveavgpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveAvgPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L1391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</p>
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 The number of output features is equal to the number of input planes.</p>
diff --git a/main/generated/torch.nn.AdaptiveAvgPool2d.html b/main/generated/torch.nn.AdaptiveAvgPool2d.html
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@@ -612,7 +612,7 @@
 <h1>AdaptiveAvgPool2d<a class="headerlink" href="#adaptiveavgpool2d" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.nn.AdaptiveAvgPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L1419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</p>
 <p>The output is of size H x W, for any input size.
 The number of output features is equal to the number of input planes.</p>
diff --git a/main/generated/torch.nn.AdaptiveAvgPool3d.html b/main/generated/torch.nn.AdaptiveAvgPool3d.html
index 646b84012ca..37415ffeb5c 100644
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+++ b/main/generated/torch.nn.AdaptiveAvgPool3d.html
@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>AdaptiveAvgPool3d<a class="headerlink" href="#adaptiveavgpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveAvgPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L1458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</p>
 <p>The output is of size D x H x W, for any input size.
 The number of output features is equal to the number of input planes.</p>
diff --git a/main/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html b/main/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html
index e04a669d94f..d277d95a247 100644
--- a/main/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html
+++ b/main/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AdaptiveLogSoftmaxWithLoss<a class="headerlink" href="#adaptivelogsoftmaxwithloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveLogSoftmaxWithLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveLogSoftmaxWithLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutoffs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">div_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/adaptive.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveLogSoftmaxWithLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutoffs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">div_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/adaptive.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Efficient softmax approximation.</p>
 <p>As described in
 <a class="reference external" href="https://arxiv.org/abs/1609.04309">Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin,
@@ -706,7 +706,7 @@ <h1>AdaptiveLogSoftmaxWithLoss<a class="headerlink" href="#adaptivelogsoftmaxwit
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/adaptive.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/adaptive.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute log probabilities for all <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="monospace">n_classes</mtext></mrow><annotation encoding="application/x-tex">\texttt{n\_classes}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7063em;vertical-align:-0.0951em;"></span><span class="mord text"><span class="mord texttt">n_classes</span></span></span></span></span></span>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -732,7 +732,7 @@ <h1>AdaptiveLogSoftmaxWithLoss<a class="headerlink" href="#adaptivelogsoftmaxwit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveLogSoftmaxWithLoss.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/adaptive.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/adaptive.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the class with the highest probability for each example in the input minibatch.</p>
 <p>This is equivalent to <code class="docutils literal notranslate"><span class="pre">self.log_prob(input).argmax(dim=1)</span></code>, but is more efficient in some cases.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.AdaptiveMaxPool1d.html b/main/generated/torch.nn.AdaptiveMaxPool1d.html
index 34b8647ae01..796c50115e8 100644
--- a/main/generated/torch.nn.AdaptiveMaxPool1d.html
+++ b/main/generated/torch.nn.AdaptiveMaxPool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AdaptiveMaxPool1d<a class="headerlink" href="#adaptivemaxpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveMaxPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L1265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</p>
 <p>The output size is <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>L</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub></mrow><annotation encoding="application/x-tex">L_{out}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, for any input size.
 The number of output features is equal to the number of input planes.</p>
diff --git a/main/generated/torch.nn.AdaptiveMaxPool2d.html b/main/generated/torch.nn.AdaptiveMaxPool2d.html
index aa953a210b7..dae045b8722 100644
--- a/main/generated/torch.nn.AdaptiveMaxPool2d.html
+++ b/main/generated/torch.nn.AdaptiveMaxPool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AdaptiveMaxPool2d<a class="headerlink" href="#adaptivemaxpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveMaxPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L1295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</p>
 <p>The output is of size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo>×</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub></mrow><annotation encoding="application/x-tex">H_{out} \times W_{out}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, for any input size.
 The number of output features is equal to the number of input planes.</p>
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index 4bfcac8743f..05b24e4f12a 100644
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@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>AdaptiveMaxPool3d<a class="headerlink" href="#adaptivemaxpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveMaxPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L1337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</p>
 <p>The output is of size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>D</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo>×</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo>×</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub></mrow><annotation encoding="application/x-tex">D_{out} \times H_{out} \times W_{out}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, for any input size.
 The number of output features is equal to the number of input planes.</p>
diff --git a/main/generated/torch.nn.AlphaDropout.html b/main/generated/torch.nn.AlphaDropout.html
index cc60fcd652a..cffac252a0a 100644
--- a/main/generated/torch.nn.AlphaDropout.html
+++ b/main/generated/torch.nn.AlphaDropout.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>AlphaDropout<a class="headerlink" href="#alphadropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AlphaDropout">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AlphaDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#AlphaDropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/dropout.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AlphaDropout" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AlphaDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#AlphaDropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AlphaDropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Alpha Dropout over the input.</p>
 <p>Alpha Dropout is a type of Dropout that maintains the self-normalizing
 property.
diff --git a/main/generated/torch.nn.AvgPool1d.html b/main/generated/torch.nn.AvgPool1d.html
index 562f50a713d..75938e1194c 100644
--- a/main/generated/torch.nn.AvgPool1d.html
+++ b/main/generated/torch.nn.AvgPool1d.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AvgPool1d<a class="headerlink" href="#avgpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AvgPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L580"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L580"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D average pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>L</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, L)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">L</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>L</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, L_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi></mrow><annotation encoding="application/x-tex">k</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span></span></span></span></span>
diff --git a/main/generated/torch.nn.AvgPool2d.html b/main/generated/torch.nn.AvgPool2d.html
index 0c33445661f..136ad59fff7 100644
--- a/main/generated/torch.nn.AvgPool2d.html
+++ b/main/generated/torch.nn.AvgPool2d.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AvgPool2d<a class="headerlink" href="#avgpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AvgPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D average pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H_{out}, W_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(kH, kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.AvgPool3d.html b/main/generated/torch.nn.AvgPool3d.html
index da1af695387..d23d2cc1ddd 100644
--- a/main/generated/torch.nn.AvgPool3d.html
+++ b/main/generated/torch.nn.AvgPool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>AvgPool3d<a class="headerlink" href="#avgpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AvgPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L767"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L767"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D average pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>D</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, D, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>D</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, D_{out}, H_{out}, W_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>k</mi><mi>D</mi><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(kD, kH, kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.BCELoss.html b/main/generated/torch.nn.BCELoss.html
index d412caa9192..428aa540a3c 100644
--- a/main/generated/torch.nn.BCELoss.html
+++ b/main/generated/torch.nn.BCELoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BCELoss<a class="headerlink" href="#bceloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BCELoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BCELoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#BCELoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BCELoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BCELoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#BCELoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BCELoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the Binary Cross Entropy between the target and
 the input probabilities:</p>
 <p>The unreduced (i.e. with <code class="xref py py-attr docutils literal notranslate"><span class="pre">reduction</span></code> set to <code class="docutils literal notranslate"><span class="pre">'none'</span></code>) loss can be described as:</p>
diff --git a/main/generated/torch.nn.BCEWithLogitsLoss.html b/main/generated/torch.nn.BCEWithLogitsLoss.html
index 4ce43c5a20e..e54bbcb814d 100644
--- a/main/generated/torch.nn.BCEWithLogitsLoss.html
+++ b/main/generated/torch.nn.BCEWithLogitsLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BCEWithLogitsLoss<a class="headerlink" href="#bcewithlogitsloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BCEWithLogitsLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BCEWithLogitsLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#BCEWithLogitsLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L704"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BCEWithLogitsLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BCEWithLogitsLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#BCEWithLogitsLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L704"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BCEWithLogitsLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>This loss combines a <cite>Sigmoid</cite> layer and the <cite>BCELoss</cite> in one single
 class. This version is more numerically stable than using a plain <cite>Sigmoid</cite>
 followed by a <cite>BCELoss</cite> as, by combining the operations into one layer,
diff --git a/main/generated/torch.nn.BatchNorm1d.html b/main/generated/torch.nn.BatchNorm1d.html
index 7ca73e040e1..0593e46bafc 100644
--- a/main/generated/torch.nn.BatchNorm1d.html
+++ b/main/generated/torch.nn.BatchNorm1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BatchNorm1d<a class="headerlink" href="#batchnorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BatchNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Batch Normalization over a 2D or 3D input.</p>
 <p>Method described in the paper
 <a class="reference external" href="https://arxiv.org/abs/1502.03167">Batch Normalization: Accelerating Deep Network Training by Reducing
diff --git a/main/generated/torch.nn.BatchNorm2d.html b/main/generated/torch.nn.BatchNorm2d.html
index ee9f0191b5b..999f775800b 100644
--- a/main/generated/torch.nn.BatchNorm2d.html
+++ b/main/generated/torch.nn.BatchNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BatchNorm2d<a class="headerlink" href="#batchnorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BatchNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Batch Normalization over a 4D input.</p>
 <p>4D is a mini-batch of 2D inputs
 with additional channel dimension. Method described in the paper
diff --git a/main/generated/torch.nn.BatchNorm3d.html b/main/generated/torch.nn.BatchNorm3d.html
index da818c10d6c..f5a0b4e5abb 100644
--- a/main/generated/torch.nn.BatchNorm3d.html
+++ b/main/generated/torch.nn.BatchNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BatchNorm3d<a class="headerlink" href="#batchnorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BatchNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Batch Normalization over a 5D input.</p>
 <p>5D is a mini-batch of 3D inputs with additional channel dimension as described in the paper
 <a class="reference external" href="https://arxiv.org/abs/1502.03167">Batch Normalization: Accelerating Deep Network Training by Reducing
diff --git a/main/generated/torch.nn.Bilinear.html b/main/generated/torch.nn.Bilinear.html
index cdc2cec2460..7ee01d78bca 100644
--- a/main/generated/torch.nn.Bilinear.html
+++ b/main/generated/torch.nn.Bilinear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Bilinear<a class="headerlink" href="#bilinear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Bilinear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in1_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in2_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/linear.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Bilinear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in1_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in2_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Bilinear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a bilinear transformation to the incoming data: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><msubsup><mi>x</mi><mn>1</mn><mi>T</mi></msubsup><mi>A</mi><msub><mi>x</mi><mn>2</mn></msub><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">y = x_1^T A x_2 + b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0894em;vertical-align:-0.2481em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-2.4519em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2481em;"><span></span></span></span></span></span></span><span class="mord mathnormal">A</span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.CELU.html b/main/generated/torch.nn.CELU.html
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           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>CELU<a class="headerlink" href="#celu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#CELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#CELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the CELU function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>CELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mi mathvariant="normal">/</mi><mi>α</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))
diff --git a/main/generated/torch.nn.CTCLoss.html b/main/generated/torch.nn.CTCLoss.html
index abb7cf56dbb..de007c89f9f 100644
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           <div class="pytorch-left-menu-search">
             
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>CTCLoss<a class="headerlink" href="#ctcloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CTCLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CTCLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">blank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_infinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CTCLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CTCLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CTCLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">blank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_infinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CTCLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CTCLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Connectionist Temporal Classification loss.</p>
 <p>Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
 probability of possible alignments of input to target, producing a loss value which is differentiable
diff --git a/main/generated/torch.nn.ChannelShuffle.html b/main/generated/torch.nn.ChannelShuffle.html
index 7ecd8f75125..d3088ebab99 100644
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>ChannelShuffle<a class="headerlink" href="#channelshuffle" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ChannelShuffle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ChannelShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">groups</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/channelshuffle.html#ChannelShuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/channelshuffle.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ChannelShuffle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ChannelShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">groups</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/channelshuffle.html#ChannelShuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/channelshuffle.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ChannelShuffle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Divides and rearranges the channels in a tensor.</p>
 <p>This operation divides the channels in a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mo>∗</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, *)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">∗</span><span class="mclose">)</span></span></span></span></span>
 into g groups as <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mfrac><mi>C</mi><mi>g</mi></mfrac><mo separator="true">,</mo><mi>g</mi><mo separator="true">,</mo><mo>∗</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, \frac{C}{g}, g, *)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.3534em;vertical-align:-0.4811em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8723em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.03588em;">g</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.07153em;">C</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.4811em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">g</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">∗</span><span class="mclose">)</span></span></span></span></span> and shuffles them,
diff --git a/main/generated/torch.nn.CircularPad1d.html b/main/generated/torch.nn.CircularPad1d.html
index 8eb0449e8d9..352cd565f20 100644
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           <div class="pytorch-left-menu-search">
             
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>CircularPad1d<a class="headerlink" href="#circularpad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CircularPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using circular padding of the input boundary.</p>
 <p>Tensor values at the beginning of the dimension are used to pad the end,
 and values at the end are used to pad the beginning. If negative padding is
diff --git a/main/generated/torch.nn.CircularPad2d.html b/main/generated/torch.nn.CircularPad2d.html
index 30f72705bc0..e7299c42ea1 100644
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     <div class="version">
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     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CircularPad2d<a class="headerlink" href="#circularpad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CircularPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using circular padding of the input boundary.</p>
 <p>Tensor values at the beginning of the dimension are used to pad the end,
 and values at the end are used to pad the beginning. If negative padding is
diff --git a/main/generated/torch.nn.CircularPad3d.html b/main/generated/torch.nn.CircularPad3d.html
index 0640b40e316..ba609259ab7 100644
--- a/main/generated/torch.nn.CircularPad3d.html
+++ b/main/generated/torch.nn.CircularPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CircularPad3d<a class="headerlink" href="#circularpad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CircularPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using circular padding of the input boundary.</p>
 <p>Tensor values at the beginning of the dimension are used to pad the end,
 and values at the end are used to pad the beginning. If negative padding is
diff --git a/main/generated/torch.nn.ConstantPad1d.html b/main/generated/torch.nn.ConstantPad1d.html
index 278cb6474e2..9e3da17ecd1 100644
--- a/main/generated/torch.nn.ConstantPad1d.html
+++ b/main/generated/torch.nn.ConstantPad1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConstantPad1d<a class="headerlink" href="#constantpad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConstantPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with a constant value.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ConstantPad2d.html b/main/generated/torch.nn.ConstantPad2d.html
index addea0ed79f..4c55faa09cd 100644
--- a/main/generated/torch.nn.ConstantPad2d.html
+++ b/main/generated/torch.nn.ConstantPad2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConstantPad2d<a class="headerlink" href="#constantpad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConstantPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with a constant value.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ConstantPad3d.html b/main/generated/torch.nn.ConstantPad3d.html
index 132e8ae08a8..ce6b8ca67d8 100644
--- a/main/generated/torch.nn.ConstantPad3d.html
+++ b/main/generated/torch.nn.ConstantPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConstantPad3d<a class="headerlink" href="#constantpad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConstantPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L323"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L323"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with a constant value.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.Conv1d.html b/main/generated/torch.nn.Conv1d.html
index c412291cd4e..fce46ee87b6 100644
--- a/main/generated/torch.nn.Conv1d.html
+++ b/main/generated/torch.nn.Conv1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Conv1d<a class="headerlink" href="#conv1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Conv1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D convolution over an input signal composed of several input
 planes.</p>
 <p>In the simplest case, the output value of the layer with input size
diff --git a/main/generated/torch.nn.Conv2d.html b/main/generated/torch.nn.Conv2d.html
index 9c1506ad31f..32d31b2a5d4 100644
--- a/main/generated/torch.nn.Conv2d.html
+++ b/main/generated/torch.nn.Conv2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Conv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D convolution over an input signal composed of several input
 planes.</p>
 <p>In the simplest case, the output value of the layer with input size
diff --git a/main/generated/torch.nn.Conv3d.html b/main/generated/torch.nn.Conv3d.html
index 110e361b161..6782951a69f 100644
--- a/main/generated/torch.nn.Conv3d.html
+++ b/main/generated/torch.nn.Conv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Conv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D convolution over an input signal composed of several input
 planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><msub><mi>C</mi><mrow><mi>i</mi><mi>n</mi></mrow></msub><mo separator="true">,</mo><mi>D</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C_{in}, D, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.0715em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">in</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.ConvTranspose1d.html b/main/generated/torch.nn.ConvTranspose1d.html
index c147fbd9ccb..217952a75a0 100644
--- a/main/generated/torch.nn.ConvTranspose1d.html
+++ b/main/generated/torch.nn.ConvTranspose1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConvTranspose1d<a class="headerlink" href="#convtranspose1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConvTranspose1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D transposed convolution operator over an input image
 composed of several input planes.</p>
 <p>This module can be seen as the gradient of Conv1d with respect to its input.
diff --git a/main/generated/torch.nn.ConvTranspose2d.html b/main/generated/torch.nn.ConvTranspose2d.html
index 86e018ea725..b73db7c7c4f 100644
--- a/main/generated/torch.nn.ConvTranspose2d.html
+++ b/main/generated/torch.nn.ConvTranspose2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConvTranspose2d<a class="headerlink" href="#convtranspose2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConvTranspose2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L986"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L986"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D transposed convolution operator over an input image
 composed of several input planes.</p>
 <p>This module can be seen as the gradient of Conv2d with respect to its input.
diff --git a/main/generated/torch.nn.ConvTranspose3d.html b/main/generated/torch.nn.ConvTranspose3d.html
index 0210210c5dc..7f7a8a3430e 100644
--- a/main/generated/torch.nn.ConvTranspose3d.html
+++ b/main/generated/torch.nn.ConvTranspose3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConvTranspose3d<a class="headerlink" href="#convtranspose3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConvTranspose3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D transposed convolution operator over an input image composed of several input
 planes.
 The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
diff --git a/main/generated/torch.nn.CosineEmbeddingLoss.html b/main/generated/torch.nn.CosineEmbeddingLoss.html
index 741ab8fe170..73662b17d72 100644
--- a/main/generated/torch.nn.CosineEmbeddingLoss.html
+++ b/main/generated/torch.nn.CosineEmbeddingLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CosineEmbeddingLoss<a class="headerlink" href="#cosineembeddingloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CosineEmbeddingLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CosineEmbeddingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CosineEmbeddingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CosineEmbeddingLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CosineEmbeddingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CosineEmbeddingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CosineEmbeddingLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the loss given input tensors
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>1</mn></msub></mrow><annotation encoding="application/x-tex">x_1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>2</mn></msub></mrow><annotation encoding="application/x-tex">x_2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> and a <cite>Tensor</cite> label <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> with values 1 or -1.
 Use (<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">y=1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1</span></span></span></span></span>) to maximize the cosine similarity of two inputs, and (<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mo>−</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">y=-1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7278em;vertical-align:-0.0833em;"></span><span class="mord">−</span><span class="mord">1</span></span></span></span></span>) otherwise.
diff --git a/main/generated/torch.nn.CosineSimilarity.html b/main/generated/torch.nn.CosineSimilarity.html
index 6eac7b404f0..adcef8637b4 100644
--- a/main/generated/torch.nn.CosineSimilarity.html
+++ b/main/generated/torch.nn.CosineSimilarity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CosineSimilarity<a class="headerlink" href="#cosinesimilarity" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CosineSimilarity">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CosineSimilarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/distance.html#CosineSimilarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/distance.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CosineSimilarity" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CosineSimilarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/distance.html#CosineSimilarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/distance.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CosineSimilarity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns cosine similarity between <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>1</mn></msub></mrow><annotation encoding="application/x-tex">x_1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>2</mn></msub></mrow><annotation encoding="application/x-tex">x_2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, computed along <cite>dim</cite>.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>similarity</mtext><mo>=</mo><mfrac><mrow><msub><mi>x</mi><mn>1</mn></msub><mo>⋅</mo><msub><mi>x</mi><mn>2</mn></msub></mrow><mrow><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">∥</mi><msub><mi>x</mi><mn>1</mn></msub><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub><mo>⋅</mo><mi mathvariant="normal">∥</mi><msub><mi>x</mi><mn>2</mn></msub><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub><mo separator="true">,</mo><mi>ϵ</mi><mo stretchy="false">)</mo></mrow></mfrac><mi mathvariant="normal">.</mi></mrow><annotation encoding="application/x-tex">\text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}.
diff --git a/main/generated/torch.nn.CrossEntropyLoss.html b/main/generated/torch.nn.CrossEntropyLoss.html
index 9e181c87006..58d3137f6ee 100644
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+++ b/main/generated/torch.nn.CrossEntropyLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
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@@ -612,7 +612,7 @@
 <h1>CrossEntropyLoss<a class="headerlink" href="#crossentropyloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CrossEntropyLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CrossEntropyLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CrossEntropyLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CrossEntropyLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CrossEntropyLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>This criterion computes the cross entropy loss between input logits
 and target.</p>
 <p>It is useful when training a classification problem with <cite>C</cite> classes.
diff --git a/main/generated/torch.nn.DataParallel.html b/main/generated/torch.nn.DataParallel.html
index 4b1a5302ff1..7fbe0f5091a 100644
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
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@@ -612,7 +612,7 @@
 <h1>DataParallel<a class="headerlink" href="#dataparallel" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.DataParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">DataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/data_parallel.html#DataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/data_parallel.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.DataParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">DataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/data_parallel.html#DataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/data_parallel.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.DataParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements data parallelism at the module level.</p>
 <p>This container parallelizes the application of the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">module</span></code> by
 splitting the input across the specified devices by chunking in the batch
diff --git a/main/generated/torch.nn.Dropout.html b/main/generated/torch.nn.Dropout.html
index 9213ba56e7c..b3d5645e5d5 100644
--- a/main/generated/torch.nn.Dropout.html
+++ b/main/generated/torch.nn.Dropout.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Dropout<a class="headerlink" href="#dropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Dropout">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/dropout.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>During training, randomly zeroes some of the elements of the input tensor with probability <code class="xref py py-attr docutils literal notranslate"><span class="pre">p</span></code>.</p>
 <p>The zeroed elements are chosen independently for each forward call and are sampled from a Bernoulli distribution.</p>
 <p>Each channel will be zeroed out independently on every forward call.</p>
diff --git a/main/generated/torch.nn.Dropout1d.html b/main/generated/torch.nn.Dropout1d.html
index d969242b439..753554ae165 100644
--- a/main/generated/torch.nn.Dropout1d.html
+++ b/main/generated/torch.nn.Dropout1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Dropout1d<a class="headerlink" href="#dropout1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Dropout1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/dropout.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels.</p>
 <p>A channel is a 1D feature map,
 e.g., the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
diff --git a/main/generated/torch.nn.Dropout2d.html b/main/generated/torch.nn.Dropout2d.html
index 2ff9e055206..eae3c00a636 100644
--- a/main/generated/torch.nn.Dropout2d.html
+++ b/main/generated/torch.nn.Dropout2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Dropout2d<a class="headerlink" href="#dropout2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Dropout2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/dropout.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels.</p>
 <p>A channel is a 2D feature map,
 e.g., the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
diff --git a/main/generated/torch.nn.Dropout3d.html b/main/generated/torch.nn.Dropout3d.html
index 963f8d2672c..c04b22fc3b0 100644
--- a/main/generated/torch.nn.Dropout3d.html
+++ b/main/generated/torch.nn.Dropout3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Dropout3d<a class="headerlink" href="#dropout3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Dropout3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/dropout.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels.</p>
 <p>A channel is a 3D feature map,
 e.g., the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
diff --git a/main/generated/torch.nn.ELU.html b/main/generated/torch.nn.ELU.html
index 861771a7927..4bdf97271bc 100644
--- a/main/generated/torch.nn.ELU.html
+++ b/main/generated/torch.nn.ELU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ELU<a class="headerlink" href="#elu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Exponential Linear Unit (ELU) function, element-wise.</p>
 <p>Method described in the paper: <a class="reference external" href="https://arxiv.org/abs/1511.07289">Fast and Accurate Deep Network Learning by Exponential Linear
 Units (ELUs)</a>.</p>
diff --git a/main/generated/torch.nn.Embedding.html b/main/generated/torch.nn.Embedding.html
index 702bd00a97a..da6c3dd9808 100644
--- a/main/generated/torch.nn.Embedding.html
+++ b/main/generated/torch.nn.Embedding.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Embedding<a class="headerlink" href="#embedding" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Embedding">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/sparse.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Embedding" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Embedding" title="Permalink to this definition">¶</a></dt>
 <dd><p>A simple lookup table that stores embeddings of a fixed dictionary and size.</p>
 <p>This module is often used to store word embeddings and retrieve them using indices.
 The input to the module is a list of indices, and the output is the corresponding
@@ -718,7 +718,7 @@ <h1>Embedding<a class="headerlink" href="#embedding" title="Permalink to this he
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Embedding.from_pretrained">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding.from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/sparse.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Embedding.from_pretrained" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding.from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Embedding.from_pretrained" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create Embedding instance from given 2-dimensional FloatTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.EmbeddingBag.html b/main/generated/torch.nn.EmbeddingBag.html
index 4b76b7244be..c991cea3913 100644
--- a/main/generated/torch.nn.EmbeddingBag.html
+++ b/main/generated/torch.nn.EmbeddingBag.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.EmbeddingBag">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">EmbeddingBag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/sparse.py#L269"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">EmbeddingBag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L269"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute sums or means of ‘bags’ of embeddings, without instantiating the intermediate embeddings.</p>
 <p>For bags of constant length, no <code class="xref py py-attr docutils literal notranslate"><span class="pre">per_sample_weights</span></code>, no indices equal to <code class="xref py py-attr docutils literal notranslate"><span class="pre">padding_idx</span></code>,
 and with 2D inputs, this class</p>
@@ -693,7 +693,7 @@ <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.EmbeddingBag.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_sample_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/sparse.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_sample_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forward pass of EmbeddingBag.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -733,7 +733,7 @@ <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.EmbeddingBag.from_pretrained">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag.from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/sparse.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag.from_pretrained" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag.from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag.from_pretrained" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create EmbeddingBag instance from given 2-dimensional FloatTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.FeatureAlphaDropout.html b/main/generated/torch.nn.FeatureAlphaDropout.html
index 8ecd3c75908..15fe422ba56 100644
--- a/main/generated/torch.nn.FeatureAlphaDropout.html
+++ b/main/generated/torch.nn.FeatureAlphaDropout.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>FeatureAlphaDropout<a class="headerlink" href="#featurealphadropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.FeatureAlphaDropout">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FeatureAlphaDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#FeatureAlphaDropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/dropout.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FeatureAlphaDropout" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FeatureAlphaDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#FeatureAlphaDropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FeatureAlphaDropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly masks out entire channels.</p>
 <p>A channel is a feature map,
 e.g. the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the batch input
diff --git a/main/generated/torch.nn.Flatten.html b/main/generated/torch.nn.Flatten.html
index 98853219999..0de133972ee 100644
--- a/main/generated/torch.nn.Flatten.html
+++ b/main/generated/torch.nn.Flatten.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Flatten<a class="headerlink" href="#flatten" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Flatten">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Flatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/flatten.html#Flatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/flatten.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Flatten" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Flatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/flatten.html#Flatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/flatten.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Flatten" title="Permalink to this definition">¶</a></dt>
 <dd><p>Flattens a contiguous range of dims into a tensor.</p>
 <p>For use with <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code>, see <a class="reference internal" href="torch.flatten.html#torch.flatten" title="torch.flatten"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.flatten()</span></code></a> for details.</p>
 <dl class="simple">
diff --git a/main/generated/torch.nn.Fold.html b/main/generated/torch.nn.Fold.html
index d9639159ebc..cbe3827f73f 100644
--- a/main/generated/torch.nn.Fold.html
+++ b/main/generated/torch.nn.Fold.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Fold<a class="headerlink" href="#fold" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Fold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Fold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/fold.html#Fold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/fold.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Fold" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Fold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/fold.html#Fold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/fold.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Fold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Combines an array of sliding local blocks into a large containing tensor.</p>
 <p>Consider a batched <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor containing sliding local blocks,
 e.g., patches of images, of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo>×</mo><mo>∏</mo><mo stretchy="false">(</mo><mtext>kernel_size</mtext><mo stretchy="false">)</mo><mo separator="true">,</mo><mi>L</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C \times  \prod(\text{kernel\_size}), L)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mop op-symbol small-op" style="position:relative;top:0em;">∏</span><span class="mopen">(</span><span class="mord text"><span class="mord">kernel_size</span></span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">L</span><span class="mclose">)</span></span></span></span></span>,
diff --git a/main/generated/torch.nn.FractionalMaxPool2d.html b/main/generated/torch.nn.FractionalMaxPool2d.html
index 46685650ad9..3ea1ec4d4c1 100644
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>FractionalMaxPool2d<a class="headerlink" href="#fractionalmaxpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.FractionalMaxPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FractionalMaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#FractionalMaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L886"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FractionalMaxPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FractionalMaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#FractionalMaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L886"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FractionalMaxPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D fractional max pooling over an input signal composed of several input planes.</p>
 <p>Fractional MaxPooling is described in detail in the paper <a class="reference external" href="https://arxiv.org/abs/1412.6071">Fractional MaxPooling</a> by Ben Graham</p>
 <p>The max-pooling operation is applied in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by a stochastic
diff --git a/main/generated/torch.nn.FractionalMaxPool3d.html b/main/generated/torch.nn.FractionalMaxPool3d.html
index cc8d8f5165b..0c770427e4b 100644
--- a/main/generated/torch.nn.FractionalMaxPool3d.html
+++ b/main/generated/torch.nn.FractionalMaxPool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>FractionalMaxPool3d<a class="headerlink" href="#fractionalmaxpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.FractionalMaxPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FractionalMaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#FractionalMaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FractionalMaxPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FractionalMaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#FractionalMaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FractionalMaxPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D fractional max pooling over an input signal composed of several input planes.</p>
 <p>Fractional MaxPooling is described in detail in the paper <a class="reference external" href="https://arxiv.org/abs/1412.6071">Fractional MaxPooling</a> by Ben Graham</p>
 <p>The max-pooling operation is applied in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>T</mi><mo>×</mo><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kT \times kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by a stochastic
diff --git a/main/generated/torch.nn.GELU.html b/main/generated/torch.nn.GELU.html
index 7e97d2917cc..2aecd321d9b 100644
--- a/main/generated/torch.nn.GELU.html
+++ b/main/generated/torch.nn.GELU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>GELU<a class="headerlink" href="#gelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">approximate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#GELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">approximate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#GELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Gaussian Error Linear Units function.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>GELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mo>∗</mo><mi mathvariant="normal">Φ</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{GELU}(x) = x * \Phi(x)
diff --git a/main/generated/torch.nn.GLU.html b/main/generated/torch.nn.GLU.html
index 4a9453434d1..776bfc635bc 100644
--- a/main/generated/torch.nn.GLU.html
+++ b/main/generated/torch.nn.GLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>GLU<a class="headerlink" href="#glu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#GLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#GLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the gated linear unit function.</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><mi>G</mi><mi>L</mi><mi>U</mi></mrow><mo stretchy="false">(</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">)</mo><mo>=</mo><mi>a</mi><mo>⊗</mo><mi>σ</mi><mo stretchy="false">(</mo><mi>b</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">{GLU}(a, b)= a \otimes \sigma(b)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal">G</span><span class="mord mathnormal" style="margin-right:0.10903em;">LU</span></span><span class="mopen">(</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">b</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">a</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">⊗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="mopen">(</span><span class="mord mathnormal">b</span><span class="mclose">)</span></span></span></span></span> where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>a</mi></mrow><annotation encoding="application/x-tex">a</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">a</span></span></span></span></span> is the first half
 of the input matrices and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>b</mi></mrow><annotation encoding="application/x-tex">b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span> is the second half.</p>
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index 5ebb718c1ab..91bea1f8f20 100644
--- a/main/generated/torch.nn.GRU.html
+++ b/main/generated/torch.nn.GRU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>GRU<a class="headerlink" href="#gru" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GRU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GRU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#GRU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/rnn.py#L1162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GRU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GRU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#GRU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L1162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GRU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
 For each element in the input sequence, each layer computes the following
 function:</p>
diff --git a/main/generated/torch.nn.GRUCell.html b/main/generated/torch.nn.GRUCell.html
index 28e7f312224..81ba4f24c32 100644
--- a/main/generated/torch.nn.GRUCell.html
+++ b/main/generated/torch.nn.GRUCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>GRUCell<a class="headerlink" href="#grucell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GRUCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GRUCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#GRUCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/rnn.py#L1720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GRUCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GRUCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#GRUCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L1720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GRUCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>A gated recurrent unit (GRU) cell.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.16em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>r</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>r</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>r</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>r</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>r</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>z</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>z</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>z</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>z</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>z</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>n</mi><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>n</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>n</mi></mrow></msub><mo>+</mo><mi>r</mi><mo>⊙</mo><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>n</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>n</mi></mrow></msub><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><msup><mi>h</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo>=</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>z</mi><mo stretchy="false">)</mo><mo>⊙</mo><mi>n</mi><mo>+</mo><mi>z</mi><mo>⊙</mo><mi>h</mi></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{array}{ll}
diff --git a/main/generated/torch.nn.GaussianNLLLoss.html b/main/generated/torch.nn.GaussianNLLLoss.html
index c7875665cbf..d6b462e0716 100644
--- a/main/generated/torch.nn.GaussianNLLLoss.html
+++ b/main/generated/torch.nn.GaussianNLLLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>GaussianNLLLoss<a class="headerlink" href="#gaussiannllloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GaussianNLLLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GaussianNLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#GaussianNLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GaussianNLLLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GaussianNLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#GaussianNLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GaussianNLLLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gaussian negative log likelihood loss.</p>
 <p>The targets are treated as samples from Gaussian distributions with
 expectations and variances predicted by the neural network. For a
diff --git a/main/generated/torch.nn.GroupNorm.html b/main/generated/torch.nn.GroupNorm.html
index 8d4e4f30bc6..994960994e8 100644
--- a/main/generated/torch.nn.GroupNorm.html
+++ b/main/generated/torch.nn.GroupNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>GroupNorm<a class="headerlink" href="#groupnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GroupNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GroupNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#GroupNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GroupNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GroupNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#GroupNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GroupNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Group Normalization over a mini-batch of inputs.</p>
 <p>This layer implements the operation as described in
 the paper <a class="reference external" href="https://arxiv.org/abs/1803.08494">Group Normalization</a></p>
diff --git a/main/generated/torch.nn.Hardshrink.html b/main/generated/torch.nn.Hardshrink.html
index b3f6a115b92..f6c40b1a92c 100644
--- a/main/generated/torch.nn.Hardshrink.html
+++ b/main/generated/torch.nn.Hardshrink.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Hardshrink<a class="headerlink" href="#hardshrink" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Hardshrink">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L740"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardshrink" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L740"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardshrink" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Hard Shrinkage (Hardshrink) function element-wise.</p>
 <p>Hardshrink is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Hardsigmoid.html b/main/generated/torch.nn.Hardsigmoid.html
index 7b31214646b..b41982ba0a6 100644
--- a/main/generated/torch.nn.Hardsigmoid.html
+++ b/main/generated/torch.nn.Hardsigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Hardsigmoid<a class="headerlink" href="#hardsigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Hardsigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardsigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardsigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Hardsigmoid function element-wise.</p>
 <p>Hardsigmoid is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Hardswish.html b/main/generated/torch.nn.Hardswish.html
index 19a4c9c9833..ffa93f763c3 100644
--- a/main/generated/torch.nn.Hardswish.html
+++ b/main/generated/torch.nn.Hardswish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Hardswish<a class="headerlink" href="#hardswish" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Hardswish">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardswish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardswish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Hardswish function, element-wise.</p>
 <p>Method described in the paper: <a class="reference external" href="https://arxiv.org/abs/1905.02244">Searching for MobileNetV3</a>.</p>
 <p>Hardswish is defined as:</p>
diff --git a/main/generated/torch.nn.Hardtanh.html b/main/generated/torch.nn.Hardtanh.html
index 77c0281e911..da53269646c 100644
--- a/main/generated/torch.nn.Hardtanh.html
+++ b/main/generated/torch.nn.Hardtanh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Hardtanh<a class="headerlink" href="#hardtanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Hardtanh">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardtanh" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardtanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the HardTanh function element-wise.</p>
 <p>HardTanh is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.HingeEmbeddingLoss.html b/main/generated/torch.nn.HingeEmbeddingLoss.html
index 2b44fb811bd..39c73680b37 100644
--- a/main/generated/torch.nn.HingeEmbeddingLoss.html
+++ b/main/generated/torch.nn.HingeEmbeddingLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>HingeEmbeddingLoss<a class="headerlink" href="#hingeembeddingloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.HingeEmbeddingLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">HingeEmbeddingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#HingeEmbeddingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.HingeEmbeddingLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">HingeEmbeddingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#HingeEmbeddingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.HingeEmbeddingLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Measures the loss given an input tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and a labels tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span>
 (containing 1 or -1).
 This is usually used for measuring whether two inputs are similar or
diff --git a/main/generated/torch.nn.HuberLoss.html b/main/generated/torch.nn.HuberLoss.html
index d24eb885b23..f6f3adfa0e1 100644
--- a/main/generated/torch.nn.HuberLoss.html
+++ b/main/generated/torch.nn.HuberLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>HuberLoss<a class="headerlink" href="#huberloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.HuberLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">HuberLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#HuberLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.HuberLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">HuberLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#HuberLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.HuberLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that uses a squared term if the absolute
 element-wise error falls below delta and a delta-scaled L1 term otherwise.
 This loss combines advantages of both <a class="reference internal" href="torch.nn.L1Loss.html#torch.nn.L1Loss" title="torch.nn.L1Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">L1Loss</span></code></a> and <a class="reference internal" href="torch.nn.MSELoss.html#torch.nn.MSELoss" title="torch.nn.MSELoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MSELoss</span></code></a>; the
diff --git a/main/generated/torch.nn.Identity.html b/main/generated/torch.nn.Identity.html
index 8fb72679bf7..2a68a68923a 100644
--- a/main/generated/torch.nn.Identity.html
+++ b/main/generated/torch.nn.Identity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Identity">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/linear.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Identity" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Identity" title="Permalink to this definition">¶</a></dt>
 <dd><p>A placeholder identity operator that is argument-insensitive.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.InstanceNorm1d.html b/main/generated/torch.nn.InstanceNorm1d.html
index 964b1eb5bb5..decf168103d 100644
--- a/main/generated/torch.nn.InstanceNorm1d.html
+++ b/main/generated/torch.nn.InstanceNorm1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>InstanceNorm1d<a class="headerlink" href="#instancenorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.InstanceNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/instancenorm.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Instance Normalization.</p>
 <p>This operation applies Instance Normalization
 over a 2D (unbatched) or 3D (batched) input as described in the paper
diff --git a/main/generated/torch.nn.InstanceNorm2d.html b/main/generated/torch.nn.InstanceNorm2d.html
index 5d0b49dfc14..3ec91efb1d9 100644
--- a/main/generated/torch.nn.InstanceNorm2d.html
+++ b/main/generated/torch.nn.InstanceNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>InstanceNorm2d<a class="headerlink" href="#instancenorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.InstanceNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/instancenorm.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Instance Normalization.</p>
 <p>This operation applies Instance Normalization
 over a 4D input (a mini-batch of 2D inputs
diff --git a/main/generated/torch.nn.InstanceNorm3d.html b/main/generated/torch.nn.InstanceNorm3d.html
index e055e4adfde..22c260155f9 100644
--- a/main/generated/torch.nn.InstanceNorm3d.html
+++ b/main/generated/torch.nn.InstanceNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>InstanceNorm3d<a class="headerlink" href="#instancenorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.InstanceNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/instancenorm.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Instance Normalization.</p>
 <p>This operation applies Instance Normalization
 over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper
diff --git a/main/generated/torch.nn.KLDivLoss.html b/main/generated/torch.nn.KLDivLoss.html
index 9530f31186b..73367bd9898 100644
--- a/main/generated/torch.nn.KLDivLoss.html
+++ b/main/generated/torch.nn.KLDivLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>KLDivLoss<a class="headerlink" href="#kldivloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.KLDivLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">KLDivLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#KLDivLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L449"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.KLDivLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">KLDivLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#KLDivLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L449"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.KLDivLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Kullback-Leibler divergence loss.</p>
 <p>For tensors of the same shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>y</mi><mtext>pred</mtext></msub><mo separator="true">,</mo><mtext> </mtext><msub><mi>y</mi><mtext>true</mtext></msub></mrow><annotation encoding="application/x-tex">y_{\text{pred}},\ y_{\text{true}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7167em;vertical-align:-0.2861em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">pred</span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace"> </span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">true</span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>,
 where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>y</mi><mtext>pred</mtext></msub></mrow><annotation encoding="application/x-tex">y_{\text{pred}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7167em;vertical-align:-0.2861em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">pred</span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span></span></span></span></span> is the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>y</mi><mtext>true</mtext></msub></mrow><annotation encoding="application/x-tex">y_{\text{true}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">true</span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is the
diff --git a/main/generated/torch.nn.L1Loss.html b/main/generated/torch.nn.L1Loss.html
index c2c6270dfae..561b33c8a31 100644
--- a/main/generated/torch.nn.L1Loss.html
+++ b/main/generated/torch.nn.L1Loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>L1Loss<a class="headerlink" href="#l1loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.L1Loss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">L1Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#L1Loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.L1Loss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">L1Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#L1Loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.L1Loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the mean absolute error (MAE) between each element in
 the input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and target <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span>.</p>
 <p>The unreduced (i.e. with <code class="xref py py-attr docutils literal notranslate"><span class="pre">reduction</span></code> set to <code class="docutils literal notranslate"><span class="pre">'none'</span></code>) loss can be described as:</p>
diff --git a/main/generated/torch.nn.LPPool1d.html b/main/generated/torch.nn.LPPool1d.html
index eeb6a30adc3..f7d57d9379b 100644
--- a/main/generated/torch.nn.LPPool1d.html
+++ b/main/generated/torch.nn.LPPool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LPPool1d<a class="headerlink" href="#lppool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LPPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D power-average pooling over an input signal composed of several input planes.</p>
 <p>On each window, the function computed is:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.LPPool2d.html b/main/generated/torch.nn.LPPool2d.html
index 5b13c845e9e..68f59be504d 100644
--- a/main/generated/torch.nn.LPPool2d.html
+++ b/main/generated/torch.nn.LPPool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LPPool2d<a class="headerlink" href="#lppool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LPPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L1132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D power-average pooling over an input signal composed of several input planes.</p>
 <p>On each window, the function computed is:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.LPPool3d.html b/main/generated/torch.nn.LPPool3d.html
index 0f62e5829f1..29510b65d54 100644
--- a/main/generated/torch.nn.LPPool3d.html
+++ b/main/generated/torch.nn.LPPool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LPPool3d<a class="headerlink" href="#lppool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LPPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L1187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D power-average pooling over an input signal composed of several input planes.</p>
 <p>On each window, the function computed is:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.LSTM.html b/main/generated/torch.nn.LSTM.html
index fe4c98073ca..1530fb710b4 100644
--- a/main/generated/torch.nn.LSTM.html
+++ b/main/generated/torch.nn.LSTM.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LSTM<a class="headerlink" href="#lstm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LSTM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/rnn.py#L795"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LSTM" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L795"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LSTM" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a multi-layer long short-term memory (LSTM) RNN to an input sequence.
 For each element in the input sequence, each layer computes the following
 function:</p>
diff --git a/main/generated/torch.nn.LSTMCell.html b/main/generated/torch.nn.LSTMCell.html
index 75fdacf0273..e0edb325396 100644
--- a/main/generated/torch.nn.LSTMCell.html
+++ b/main/generated/torch.nn.LSTMCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LSTMCell<a class="headerlink" href="#lstmcell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LSTMCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LSTMCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#LSTMCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/rnn.py#L1610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LSTMCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LSTMCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#LSTMCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L1610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LSTMCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>A long short-term memory (LSTM) cell.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.16em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>i</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>i</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>i</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>i</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>i</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>f</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>f</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>f</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>f</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>f</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>g</mi><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>g</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>g</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>g</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>g</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>o</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>o</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>o</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>o</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>o</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><msup><mi>c</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo>=</mo><mi>f</mi><mo>⊙</mo><mi>c</mi><mo>+</mo><mi>i</mi><mo>⊙</mo><mi>g</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><msup><mi>h</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo>=</mo><mi>o</mi><mo>⊙</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><msup><mi>c</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{array}{ll}
diff --git a/main/generated/torch.nn.LayerNorm.html b/main/generated/torch.nn.LayerNorm.html
index 9d3a6673aae..1e6961d438d 100644
--- a/main/generated/torch.nn.LayerNorm.html
+++ b/main/generated/torch.nn.LayerNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LayerNorm<a class="headerlink" href="#layernorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LayerNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LayerNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#LayerNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LayerNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LayerNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#LayerNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LayerNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Layer Normalization over a mini-batch of inputs.</p>
 <p>This layer implements the operation as described in
 the paper <a class="reference external" href="https://arxiv.org/abs/1607.06450">Layer Normalization</a></p>
diff --git a/main/generated/torch.nn.LazyBatchNorm1d.html b/main/generated/torch.nn.LazyBatchNorm1d.html
index 304e9e14cf5..5eaa9cefe45 100644
--- a/main/generated/torch.nn.LazyBatchNorm1d.html
+++ b/main/generated/torch.nn.LazyBatchNorm1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>LazyBatchNorm1d<a class="headerlink" href="#lazybatchnorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d" title="torch.nn.BatchNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.BatchNorm1d</span></code></a> module with lazy initialization.</p>
 <p>Lazy initialization based on the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d" title="torch.nn.BatchNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm1d</span></code></a> that is inferred
 from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyBatchNorm1d<a class="headerlink" href="#lazybatchnorm1d" title="Permalin
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm1d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d" title="torch.nn.modules.batchnorm.BatchNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm1d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyBatchNorm2d.html b/main/generated/torch.nn.LazyBatchNorm2d.html
index 070f67eda71..c9ace864fa0 100644
--- a/main/generated/torch.nn.LazyBatchNorm2d.html
+++ b/main/generated/torch.nn.LazyBatchNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyBatchNorm2d<a class="headerlink" href="#lazybatchnorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.BatchNorm2d</span></code></a> module with lazy initialization.</p>
 <p>Lazy initialization is done for the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a> that is inferred
 from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyBatchNorm2d<a class="headerlink" href="#lazybatchnorm2d" title="Permalin
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm2d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.modules.batchnorm.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyBatchNorm3d.html b/main/generated/torch.nn.LazyBatchNorm3d.html
index 4d665abbf29..a7a6e3321e5 100644
--- a/main/generated/torch.nn.LazyBatchNorm3d.html
+++ b/main/generated/torch.nn.LazyBatchNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyBatchNorm3d<a class="headerlink" href="#lazybatchnorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L566"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L566"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.BatchNorm3d</span></code></a> module with lazy initialization.</p>
 <p>Lazy initialization is done for the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm3d</span></code></a> that is inferred
 from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyBatchNorm3d<a class="headerlink" href="#lazybatchnorm3d" title="Permalin
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm3d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.modules.batchnorm.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm3d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConv1d.html b/main/generated/torch.nn.LazyConv1d.html
index f114423bf33..80ce4890ea7 100644
--- a/main/generated/torch.nn.LazyConv1d.html
+++ b/main/generated/torch.nn.LazyConv1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConv1d<a class="headerlink" href="#lazyconv1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConv1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L1455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.Conv1d.html#torch.nn.Conv1d" title="torch.nn.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Conv1d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.Conv1d.html#torch.nn.Conv1d" title="torch.nn.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a> is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite> and <cite>bias</cite>.</p>
@@ -643,7 +643,7 @@ <h1>LazyConv1d<a class="headerlink" href="#lazyconv1d" title="Permalink to this
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConv1d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.Conv1d.html#torch.nn.Conv1d" title="torch.nn.modules.conv.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConv2d.html b/main/generated/torch.nn.LazyConv2d.html
index 4a68696db13..17822960b9d 100644
--- a/main/generated/torch.nn.LazyConv2d.html
+++ b/main/generated/torch.nn.LazyConv2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConv2d<a class="headerlink" href="#lazyconv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L1524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.Conv2d.html#torch.nn.Conv2d" title="torch.nn.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Conv2d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.Conv2d.html#torch.nn.Conv2d" title="torch.nn.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a> that is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite> and <cite>bias</cite>.</p>
@@ -643,7 +643,7 @@ <h1>LazyConv2d<a class="headerlink" href="#lazyconv2d" title="Permalink to this
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConv2d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.Conv2d.html#torch.nn.Conv2d" title="torch.nn.modules.conv.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConv3d.html b/main/generated/torch.nn.LazyConv3d.html
index c6618ed1c0e..e1087b55f8e 100644
--- a/main/generated/torch.nn.LazyConv3d.html
+++ b/main/generated/torch.nn.LazyConv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConv3d<a class="headerlink" href="#lazyconv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L1593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.Conv3d.html#torch.nn.Conv3d" title="torch.nn.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Conv3d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.Conv3d.html#torch.nn.Conv3d" title="torch.nn.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code></a> that is inferred from
 the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -644,7 +644,7 @@ <h1>LazyConv3d<a class="headerlink" href="#lazyconv3d" title="Permalink to this
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConv3d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.Conv3d.html#torch.nn.Conv3d" title="torch.nn.modules.conv.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConvTranspose1d.html b/main/generated/torch.nn.LazyConvTranspose1d.html
index 494eca0bb14..dcacba709ae 100644
--- a/main/generated/torch.nn.LazyConvTranspose1d.html
+++ b/main/generated/torch.nn.LazyConvTranspose1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConvTranspose1d<a class="headerlink" href="#lazyconvtranspose1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L1663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.ConvTranspose1d.html#torch.nn.ConvTranspose1d" title="torch.nn.ConvTranspose1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.ConvTranspose1d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.ConvTranspose1d.html#torch.nn.ConvTranspose1d" title="torch.nn.ConvTranspose1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose1d</span></code></a> that is inferred from
 the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyConvTranspose1d<a class="headerlink" href="#lazyconvtranspose1d" title="
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose1d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.ConvTranspose1d.html#torch.nn.ConvTranspose1d" title="torch.nn.modules.conv.ConvTranspose1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose1d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConvTranspose2d.html b/main/generated/torch.nn.LazyConvTranspose2d.html
index 845e2971b73..fad5ffd6e33 100644
--- a/main/generated/torch.nn.LazyConvTranspose2d.html
+++ b/main/generated/torch.nn.LazyConvTranspose2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConvTranspose2d<a class="headerlink" href="#lazyconvtranspose2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L1732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d" title="torch.nn.ConvTranspose2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.ConvTranspose2d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d" title="torch.nn.ConvTranspose2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose2d</span></code></a> is inferred from
 the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyConvTranspose2d<a class="headerlink" href="#lazyconvtranspose2d" title="
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose2d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L986"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L986"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d" title="torch.nn.modules.conv.ConvTranspose2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose2d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConvTranspose3d.html b/main/generated/torch.nn.LazyConvTranspose3d.html
index 2fcaceb7cef..9ba926c0fff 100644
--- a/main/generated/torch.nn.LazyConvTranspose3d.html
+++ b/main/generated/torch.nn.LazyConvTranspose3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConvTranspose3d<a class="headerlink" href="#lazyconvtranspose3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L1801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.ConvTranspose3d.html#torch.nn.ConvTranspose3d" title="torch.nn.ConvTranspose3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.ConvTranspose3d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.ConvTranspose3d.html#torch.nn.ConvTranspose3d" title="torch.nn.ConvTranspose3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose3d</span></code></a> is inferred from
 the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyConvTranspose3d<a class="headerlink" href="#lazyconvtranspose3d" title="
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose3d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/conv.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.ConvTranspose3d.html#torch.nn.ConvTranspose3d" title="torch.nn.modules.conv.ConvTranspose3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose3d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyInstanceNorm1d.html b/main/generated/torch.nn.LazyInstanceNorm1d.html
index e748c866d21..e11924157ea 100644
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+++ b/main/generated/torch.nn.LazyInstanceNorm1d.html
@@ -300,7 +300,7 @@
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyInstanceNorm1d<a class="headerlink" href="#lazyinstancenorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/instancenorm.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.InstanceNorm1d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm1d</span></code></a> is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite>, <cite>bias</cite>, <cite>running_mean</cite> and <cite>running_var</cite>.</p>
@@ -644,7 +644,7 @@ <h1>LazyInstanceNorm1d<a class="headerlink" href="#lazyinstancenorm1d" title="Pe
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm1d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/instancenorm.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.modules.instancenorm.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm1d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyInstanceNorm2d.html b/main/generated/torch.nn.LazyInstanceNorm2d.html
index e9dea694da9..e7dcf89adb6 100644
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@@ -300,7 +300,7 @@
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyInstanceNorm2d<a class="headerlink" href="#lazyinstancenorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/instancenorm.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.InstanceNorm2d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm2d</span></code></a> is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite>, <cite>bias</cite>,
@@ -645,7 +645,7 @@ <h1>LazyInstanceNorm2d<a class="headerlink" href="#lazyinstancenorm2d" title="Pe
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm2d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/instancenorm.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.modules.instancenorm.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm2d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyInstanceNorm3d.html b/main/generated/torch.nn.LazyInstanceNorm3d.html
index ed30a685952..9450f684e72 100644
--- a/main/generated/torch.nn.LazyInstanceNorm3d.html
+++ b/main/generated/torch.nn.LazyInstanceNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyInstanceNorm3d<a class="headerlink" href="#lazyinstancenorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/instancenorm.py#L436"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L436"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.InstanceNorm3d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm3d</span></code></a> is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite>, <cite>bias</cite>,
@@ -645,7 +645,7 @@ <h1>LazyInstanceNorm3d<a class="headerlink" href="#lazyinstancenorm3d" title="Pe
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm3d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/instancenorm.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.modules.instancenorm.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm3d</span></code></a></p>
 </dd></dl>
 
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index 268f5625d16..0a4ad4ae4cb 100644
--- a/main/generated/torch.nn.LazyLinear.html
+++ b/main/generated/torch.nn.LazyLinear.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyLinear<a class="headerlink" href="#lazylinear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyLinear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyLinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#LazyLinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/linear.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyLinear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyLinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#LazyLinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyLinear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Linear</span></code></a> module where <cite>in_features</cite> is inferred.</p>
 <p>In this module, the <cite>weight</cite> and <cite>bias</cite> are of <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.UninitializedParameter</span></code>
 class. They will be initialized after the first call to <code class="docutils literal notranslate"><span class="pre">forward</span></code> is done and the
@@ -687,7 +687,7 @@ <h1>LazyLinear<a class="headerlink" href="#lazylinear" title="Permalink to this
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyLinear.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/linear.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyLinear.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyLinear.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.modules.linear.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LeakyReLU.html b/main/generated/torch.nn.LeakyReLU.html
index 23ef110e53a..83b6fdcc398 100644
--- a/main/generated/torch.nn.LeakyReLU.html
+++ b/main/generated/torch.nn.LeakyReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>LeakyReLU<a class="headerlink" href="#leakyrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LeakyReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LeakyReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LeakyReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L783"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LeakyReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LeakyReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LeakyReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L783"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LeakyReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the LeakyReLU function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>LeakyReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mtext>negative_slope</mtext><mo>∗</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)
diff --git a/main/generated/torch.nn.Linear.html b/main/generated/torch.nn.Linear.html
index a66a4dca200..f87c68ebb2f 100644
--- a/main/generated/torch.nn.Linear.html
+++ b/main/generated/torch.nn.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/linear.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies an affine linear transformation to the incoming data: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>x</mi><msup><mi>A</mi><mi>T</mi></msup><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">y = xA^T + b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.9247em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">x</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span>.</p>
 <p>This module supports <a class="reference internal" href="../notes/cuda.html#tf32-on-ampere"><span class="std std-ref">TensorFloat32</span></a>.</p>
 <p>On certain ROCm devices, when using float16 inputs this module will use <a class="reference internal" href="../notes/numerical_accuracy.html#fp16-on-mi200"><span class="std std-ref">different precision</span></a> for backward.</p>
diff --git a/main/generated/torch.nn.LocalResponseNorm.html b/main/generated/torch.nn.LocalResponseNorm.html
index 65d2d22984a..638bce42371 100644
--- a/main/generated/torch.nn.LocalResponseNorm.html
+++ b/main/generated/torch.nn.LocalResponseNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LocalResponseNorm<a class="headerlink" href="#localresponsenorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LocalResponseNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LocalResponseNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#LocalResponseNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LocalResponseNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LocalResponseNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#LocalResponseNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LocalResponseNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies local response normalization over an input signal.</p>
 <p>The input signal is composed of several input planes, where channels occupy the second dimension.
 Applies normalization across channels.</p>
diff --git a/main/generated/torch.nn.LogSigmoid.html b/main/generated/torch.nn.LogSigmoid.html
index ba6018c7794..d4548a9d836 100644
--- a/main/generated/torch.nn.LogSigmoid.html
+++ b/main/generated/torch.nn.LogSigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>LogSigmoid<a class="headerlink" href="#logsigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LogSigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LogSigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LogSigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LogSigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LogSigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LogSigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LogSigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Logsigmoid function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>LogSigmoid</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>log</mi><mo>⁡</mo><mrow><mo fence="true">(</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac><mo fence="true">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
diff --git a/main/generated/torch.nn.LogSoftmax.html b/main/generated/torch.nn.LogSoftmax.html
index 79f35b302aa..fb101fe030f 100644
--- a/main/generated/torch.nn.LogSoftmax.html
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@@ -612,7 +612,7 @@
 <h1>LogSoftmax<a class="headerlink" href="#logsoftmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LogSoftmax">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LogSoftmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LogSoftmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L1708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LogSoftmax" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LogSoftmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LogSoftmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LogSoftmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>log</mi><mo>⁡</mo><mo stretchy="false">(</mo><mtext>Softmax</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\log(\text{Softmax}(x))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">lo<span style="margin-right:0.01389em;">g</span></span><span class="mopen">(</span><span class="mord text"><span class="mord">Softmax</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">))</span></span></span></span></span> function to an n-dimensional input Tensor.</p>
 <p>The LogSoftmax formulation can be simplified as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.MSELoss.html b/main/generated/torch.nn.MSELoss.html
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@@ -612,7 +612,7 @@
 <h1>MSELoss<a class="headerlink" href="#mseloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MSELoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MSELoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MSELoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MSELoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MSELoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MSELoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MSELoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the mean squared error (squared L2 norm) between
 each element in the input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and target <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span>.</p>
 <p>The unreduced (i.e. with <code class="xref py py-attr docutils literal notranslate"><span class="pre">reduction</span></code> set to <code class="docutils literal notranslate"><span class="pre">'none'</span></code>) loss can be described as:</p>
diff --git a/main/generated/torch.nn.MarginRankingLoss.html b/main/generated/torch.nn.MarginRankingLoss.html
index 0eecdcb7627..0eb87475c75 100644
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     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>MarginRankingLoss<a class="headerlink" href="#marginrankingloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MarginRankingLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MarginRankingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MarginRankingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MarginRankingLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MarginRankingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MarginRankingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MarginRankingLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the loss given
 inputs <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>1</mn></mrow><annotation encoding="application/x-tex">x1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">1</span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>2</mn></mrow><annotation encoding="application/x-tex">x2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">2</span></span></span></span></span>, two 1D mini-batch or 0D <cite>Tensors</cite>,
 and a label 1D mini-batch or 0D <cite>Tensor</cite> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> (containing 1 or -1).</p>
diff --git a/main/generated/torch.nn.MaxPool1d.html b/main/generated/torch.nn.MaxPool1d.html
index 91dc690c573..f2b4970fbe2 100644
--- a/main/generated/torch.nn.MaxPool1d.html
+++ b/main/generated/torch.nn.MaxPool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>MaxPool1d<a class="headerlink" href="#maxpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D max pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>L</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, L)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">L</span><span class="mclose">)</span></span></span></span></span>
 and output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>L</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, L_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> can be precisely described as:</p>
diff --git a/main/generated/torch.nn.MaxPool2d.html b/main/generated/torch.nn.MaxPool2d.html
index 10fb178eb8f..f7a5c1927a4 100644
--- a/main/generated/torch.nn.MaxPool2d.html
+++ b/main/generated/torch.nn.MaxPool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>MaxPool2d<a class="headerlink" href="#maxpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D max pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H_{out}, W_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(kH, kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.MaxPool3d.html b/main/generated/torch.nn.MaxPool3d.html
index cb6a456306e..ff627f4d6f4 100644
--- a/main/generated/torch.nn.MaxPool3d.html
+++ b/main/generated/torch.nn.MaxPool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MaxPool3d<a class="headerlink" href="#maxpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D max pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>D</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, D, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>D</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, D_{out}, H_{out}, W_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>k</mi><mi>D</mi><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(kD, kH, kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.MaxUnpool1d.html b/main/generated/torch.nn.MaxUnpool1d.html
index f11b721f7db..311163754b3 100644
--- a/main/generated/torch.nn.MaxUnpool1d.html
+++ b/main/generated/torch.nn.MaxUnpool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MaxUnpool1d<a class="headerlink" href="#maxunpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxUnpool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a partial inverse of <a class="reference internal" href="torch.nn.MaxPool1d.html#torch.nn.MaxPool1d" title="torch.nn.MaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a>.</p>
 <p><a class="reference internal" href="torch.nn.MaxPool1d.html#torch.nn.MaxPool1d" title="torch.nn.MaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a> is not fully invertible, since the non-maximal values are lost.</p>
 <p><a class="reference internal" href="#torch.nn.MaxUnpool1d" title="torch.nn.MaxUnpool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool1d</span></code></a> takes in as input the output of <a class="reference internal" href="torch.nn.MaxPool1d.html#torch.nn.MaxPool1d" title="torch.nn.MaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a>
diff --git a/main/generated/torch.nn.MaxUnpool2d.html b/main/generated/torch.nn.MaxUnpool2d.html
index 15e972459b7..ecfd934ed40 100644
--- a/main/generated/torch.nn.MaxUnpool2d.html
+++ b/main/generated/torch.nn.MaxUnpool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MaxUnpool2d<a class="headerlink" href="#maxunpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxUnpool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a partial inverse of <a class="reference internal" href="torch.nn.MaxPool2d.html#torch.nn.MaxPool2d" title="torch.nn.MaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a>.</p>
 <p><a class="reference internal" href="torch.nn.MaxPool2d.html#torch.nn.MaxPool2d" title="torch.nn.MaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a> is not fully invertible, since the non-maximal values are lost.</p>
 <p><a class="reference internal" href="#torch.nn.MaxUnpool2d" title="torch.nn.MaxUnpool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool2d</span></code></a> takes in as input the output of <a class="reference internal" href="torch.nn.MaxPool2d.html#torch.nn.MaxPool2d" title="torch.nn.MaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a>
diff --git a/main/generated/torch.nn.MaxUnpool3d.html b/main/generated/torch.nn.MaxUnpool3d.html
index 4590f0a7719..d791ec8a038 100644
--- a/main/generated/torch.nn.MaxUnpool3d.html
+++ b/main/generated/torch.nn.MaxUnpool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MaxUnpool3d<a class="headerlink" href="#maxunpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxUnpool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pooling.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a partial inverse of <a class="reference internal" href="torch.nn.MaxPool3d.html#torch.nn.MaxPool3d" title="torch.nn.MaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code></a>.</p>
 <p><a class="reference internal" href="torch.nn.MaxPool3d.html#torch.nn.MaxPool3d" title="torch.nn.MaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code></a> is not fully invertible, since the non-maximal values are lost.
 <a class="reference internal" href="#torch.nn.MaxUnpool3d" title="torch.nn.MaxUnpool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool3d</span></code></a> takes in as input the output of <a class="reference internal" href="torch.nn.MaxPool3d.html#torch.nn.MaxPool3d" title="torch.nn.MaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code></a>
diff --git a/main/generated/torch.nn.Mish.html b/main/generated/torch.nn.Mish.html
index 5c4d4cf54c9..12cd5d8757b 100644
--- a/main/generated/torch.nn.Mish.html
+++ b/main/generated/torch.nn.Mish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Mish<a class="headerlink" href="#mish" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Mish">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Mish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Mish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Mish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Mish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Mish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Mish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Mish function, element-wise.</p>
 <p>Mish: A Self Regularized Non-Monotonic Neural Activation Function.</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Module.html b/main/generated/torch.nn.Module.html
index 8fea78e1357..c352739cebc 100644
--- a/main/generated/torch.nn.Module.html
+++ b/main/generated/torch.nn.Module.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Module">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for all neural network modules.</p>
 <p>Your models should also subclass this class.</p>
 <p>Modules can also contain other Modules, allowing them to be nested in
@@ -646,7 +646,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.add_module">
-<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.add_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.add_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.add_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.add_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a child module to the current module.</p>
 <p>The module can be accessed as an attribute using the given name.</p>
 <dl class="field-list simple">
@@ -662,7 +662,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p>
 <p>Typical use includes initializing the parameters of a model
 (see also <a class="reference internal" href="../nn.init.html#nn-init-doc"><span class="std std-ref">torch.nn.init</span></a>).</p>
@@ -704,7 +704,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.bfloat16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.bfloat16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -722,7 +722,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.buffers">
-<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -748,7 +748,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.children">
-<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.children"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.children"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -762,7 +762,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.compile">
-<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.compile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compile this Module’s forward using <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
 <p>This Module’s <cite>__call__</cite> method is compiled and all arguments are passed as-is
 to <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
@@ -771,7 +771,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the CPU.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -789,7 +789,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.cuda"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.cuda"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the GPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -814,7 +814,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -832,7 +832,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.eval">
-<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in evaluation mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -853,7 +853,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the extra representation of the module.</p>
 <p>To print customized extra information, you should re-implement
 this method in your own modules. Both single-line and multi-line
@@ -867,7 +867,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -885,7 +885,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the computation performed at every call.</p>
 <p>Should be overridden by all subclasses.</p>
 <div class="admonition note">
@@ -901,7 +901,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.get_buffer">
-<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -928,7 +928,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.get_extra_state">
-<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return any extra state to include in the module’s state_dict.</p>
 <p>Implement this and a corresponding <a class="reference internal" href="#torch.nn.Module.set_extra_state" title="torch.nn.Module.set_extra_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_extra_state()</span></code></a> for your module
 if you need to store extra state. This function is called when building the
@@ -949,7 +949,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.get_parameter">
-<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -976,7 +976,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.get_submodule">
-<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>For example, let’s say you have an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> <code class="docutils literal notranslate"><span class="pre">A</span></code> that
 looks like this:</p>
@@ -1025,7 +1025,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.half"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.half"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1043,7 +1043,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.ipu">
-<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.ipu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.ipu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.ipu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.ipu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the IPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1068,7 +1068,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy parameters and buffers from <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> into this module and its descendants.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">strict</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then
 the keys of <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> must exactly match the keys returned
@@ -1123,7 +1123,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.modules">
-<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1155,7 +1155,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.mtia">
-<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.mtia"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.mtia" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.mtia"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.mtia" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the MTIA.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1180,7 +1180,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1209,7 +1209,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.named_children">
-<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_children"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_children"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1229,7 +1229,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.named_modules">
-<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1266,7 +1266,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1296,7 +1296,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.parameters">
-<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters.</p>
 <p>This is typically passed to an optimizer.</p>
 <dl class="field-list simple">
@@ -1323,7 +1323,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_backward_hook">
-<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>This function is deprecated in favor of <a class="reference internal" href="#torch.nn.Module.register_full_backward_hook" title="torch.nn.Module.register_full_backward_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">register_full_backward_hook()</span></code></a> and
 the behavior of this function will change in future versions.</p>
@@ -1340,7 +1340,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_buffer">
-<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a buffer to the module.</p>
 <p>This is typically used to register a buffer that should not to be
 considered a model parameter. For example, BatchNorm’s <code class="docutils literal notranslate"><span class="pre">running_mean</span></code>
@@ -1372,7 +1372,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_forward_hook">
-<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_forward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_forward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_forward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_forward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward hook on the module.</p>
 <p>The hook will be called every time after <a class="reference internal" href="#torch.nn.Module.forward" title="torch.nn.Module.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> has computed an output.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> or not specified, the input contains only
@@ -1423,7 +1423,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_forward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_forward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_forward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward pre-hook on the module.</p>
 <p>The hook will be called every time before <a class="reference internal" href="#torch.nn.Module.forward" title="torch.nn.Module.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> is invoked.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is false or not specified, the input contains only
@@ -1473,7 +1473,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_full_backward_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_full_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_full_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>The hook will be called every time the gradients with respect to a module
 are computed, i.e. the hook will execute if and only if the gradients with
@@ -1524,7 +1524,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_full_backward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_full_backward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_full_backward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook on the module.</p>
 <p>The hook will be called every time the gradients for the module are computed.
 The hook should have the following signature:</p>
@@ -1570,7 +1570,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_load_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_load_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook to be run after module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, incompatible_keys) -&gt; None</p>
@@ -1600,7 +1600,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_load_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_load_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook to be run before module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -&gt; None  # noqa: B950</p>
@@ -1616,7 +1616,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_module">
-<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Alias for <a class="reference internal" href="#torch.nn.Module.add_module" title="torch.nn.Module.add_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
@@ -1624,7 +1624,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_parameter">
-<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a parameter to the module.</p>
 <p>The parameter can be accessed as an attribute using given name.</p>
 <dl class="field-list simple">
@@ -1643,7 +1643,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook for the <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata) -&gt; None</p>
@@ -1654,7 +1654,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook for the <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, prefix, keep_vars) -&gt; None</p>
@@ -1666,7 +1666,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.requires_grad_">
-<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.requires_grad_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.requires_grad_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.requires_grad_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.requires_grad_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Change if autograd should record operations on parameters in this module.</p>
 <p>This method sets the parameters’ <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad</span></code> attributes
 in-place.</p>
@@ -1690,7 +1690,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.set_extra_state">
-<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.set_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.set_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.set_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.set_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p>
 <p>This function is called from <a class="reference internal" href="#torch.nn.Module.load_state_dict" title="torch.nn.Module.load_state_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> to handle any extra state
 found within the <cite>state_dict</cite>. Implement this function and a corresponding
@@ -1705,7 +1705,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.set_submodule">
-<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.set_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.set_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.set_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.set_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1763,7 +1763,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.share_memory">
-<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.share_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.share_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.share_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.share_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.Tensor.share_memory_.html#torch.Tensor.share_memory_" title="torch.Tensor.share_memory_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1774,7 +1774,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">T_destination</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">T_destination</span></span></span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">T_destination</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">T_destination</span></span></span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.state_dict" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
 <dd><p>Return a dictionary containing references to the whole state of the module.</p>
@@ -1829,7 +1829,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensor_attributes.html#torch.device" title="torch.device"><span class="pre">device</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensor_attributes.html#torch.device" title="torch.device"><span class="pre">device</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.to" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span></dt>
 <dt class="sig sig-object py">
@@ -1838,22 +1838,22 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 <p>This can be called as</p>
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <p>Its signature is similar to <a class="reference internal" href="torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.to()</span></code></a>, but only accepts
@@ -1932,7 +1932,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.to_empty">
-<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to_empty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.to_empty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to_empty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.to_empty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move the parameters and buffers to the specified device without copying storage.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1954,7 +1954,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.train">
-<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.train" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.train" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in training mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -1976,7 +1976,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1997,7 +1997,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.xpu">
-<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.xpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.xpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.xpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.xpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the XPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing optimizer if the module will
@@ -2022,7 +2022,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset gradients of all model parameters.</p>
 <p>See similar function under <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> for more context.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ModuleDict.html b/main/generated/torch.nn.ModuleDict.html
index 74f73dae9a9..6aa59521300 100644
--- a/main/generated/torch.nn.ModuleDict.html
+++ b/main/generated/torch.nn.ModuleDict.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ModuleDict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ModuleDict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds submodules in a dictionary.</p>
 <p><a class="reference internal" href="#torch.nn.ModuleDict" title="torch.nn.ModuleDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModuleDict</span></code></a> can be indexed like a regular Python dictionary,
 but modules it contains are properly registered, and will be visible by all
@@ -655,7 +655,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.clear">
-<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.clear" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.clear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove all items from the ModuleDict.</p>
 <dl class="field-list simple">
 </dl>
@@ -663,7 +663,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.items">
-<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L531"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.items" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L531"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.items" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ModuleDict key/value pairs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -674,7 +674,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.keys">
-<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L526"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.keys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L526"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.keys" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ModuleDict keys.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -685,7 +685,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.pop">
-<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.pop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove key from the ModuleDict and return its module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -699,7 +699,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.update">
-<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.update" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update the <a class="reference internal" href="#torch.nn.ModuleDict" title="torch.nn.ModuleDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModuleDict</span></code></a> with key-value pairs from a mapping, overwriting existing keys.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -716,7 +716,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.values">
-<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.values" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ModuleDict values.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.ModuleList.html b/main/generated/torch.nn.ModuleList.html
index 9425a61f291..808b67d715e 100644
--- a/main/generated/torch.nn.ModuleList.html
+++ b/main/generated/torch.nn.ModuleList.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ModuleList<a class="headerlink" href="#modulelist" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ModuleList">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ModuleList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ModuleList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds submodules in a list.</p>
 <p><a class="reference internal" href="#torch.nn.ModuleList" title="torch.nn.ModuleList"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModuleList</span></code></a> can be indexed like a regular Python list, but
 modules it contains are properly registered, and will be visible by all
@@ -637,7 +637,7 @@ <h1>ModuleList<a class="headerlink" href="#modulelist" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleList.append">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.append" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.append" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append a given module to the end of the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -651,7 +651,7 @@ <h1>ModuleList<a class="headerlink" href="#modulelist" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleList.extend">
-<span class="sig-name descname"><span class="pre">extend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.extend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.extend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.extend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.extend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append modules from a Python iterable to the end of the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -665,7 +665,7 @@ <h1>ModuleList<a class="headerlink" href="#modulelist" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleList.insert">
-<span class="sig-name descname"><span class="pre">insert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.insert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L395"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.insert" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">insert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.insert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L395"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.insert" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a given module before a given index in the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.MultiLabelMarginLoss.html b/main/generated/torch.nn.MultiLabelMarginLoss.html
index 38d3f6975c1..b6e9f3bffb6 100644
--- a/main/generated/torch.nn.MultiLabelMarginLoss.html
+++ b/main/generated/torch.nn.MultiLabelMarginLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiLabelMarginLoss<a class="headerlink" href="#multilabelmarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MultiLabelMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiLabelMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiLabelMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L898"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiLabelMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiLabelMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiLabelMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L898"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiLabelMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that optimizes a multi-class multi-classification
 hinge loss (margin-based loss) between input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> (a 2D mini-batch <cite>Tensor</cite>)
 and output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> (which is a 2D <cite>Tensor</cite> of target class indices).
diff --git a/main/generated/torch.nn.MultiLabelSoftMarginLoss.html b/main/generated/torch.nn.MultiLabelSoftMarginLoss.html
index 935f7dadc15..6b2dfcc6e85 100644
--- a/main/generated/torch.nn.MultiLabelSoftMarginLoss.html
+++ b/main/generated/torch.nn.MultiLabelSoftMarginLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiLabelSoftMarginLoss<a class="headerlink" href="#multilabelsoftmarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MultiLabelSoftMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiLabelSoftMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiLabelSoftMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1305"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiLabelSoftMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiLabelSoftMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiLabelSoftMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1305"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiLabelSoftMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that optimizes a multi-label one-versus-all
 loss based on max-entropy, between input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and target <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> of size
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mclose">)</span></span></span></span></span>.
diff --git a/main/generated/torch.nn.MultiMarginLoss.html b/main/generated/torch.nn.MultiMarginLoss.html
index 9714153b150..ad446098a54 100644
--- a/main/generated/torch.nn.MultiMarginLoss.html
+++ b/main/generated/torch.nn.MultiMarginLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiMarginLoss<a class="headerlink" href="#multimarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MultiMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that optimizes a multi-class classification hinge
 loss (margin-based loss) between input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> (a 2D mini-batch <cite>Tensor</cite>) and
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> (which is a 1D tensor of target class indices,
diff --git a/main/generated/torch.nn.MultiheadAttention.html b/main/generated/torch.nn.MultiheadAttention.html
index 322c5cb488f..f4ebe5b3492 100644
--- a/main/generated/torch.nn.MultiheadAttention.html
+++ b/main/generated/torch.nn.MultiheadAttention.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MultiheadAttention">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiheadAttention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_bias_kv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_zero_attn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiheadAttention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_bias_kv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_zero_attn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention" title="Permalink to this definition">¶</a></dt>
 <dd><p>Allows the model to jointly attend to information from different representation subspaces.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -675,7 +675,7 @@ <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Pe
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.MultiheadAttention.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">need_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_attn_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L1139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">need_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_attn_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute attention outputs using query, key, and value embeddings.</p>
 <blockquote>
 <div><p>Supports optional parameters for padding, masks and attention weights.</p>
@@ -751,7 +751,7 @@ <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.MultiheadAttention.merge_masks">
-<span class="sig-name descname"><span class="pre">merge_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention.merge_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L1399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention.merge_masks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">merge_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention.merge_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention.merge_masks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine mask type and combine masks if necessary.</p>
 <p>If only one mask is provided, that mask
 and the corresponding mask type will be returned. If both masks are provided, they will be both
diff --git a/main/generated/torch.nn.NLLLoss.html b/main/generated/torch.nn.NLLLoss.html
index e27639d9fdb..7b2ece2b936 100644
--- a/main/generated/torch.nn.NLLLoss.html
+++ b/main/generated/torch.nn.NLLLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>NLLLoss<a class="headerlink" href="#nllloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.NLLLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">NLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#NLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.NLLLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">NLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#NLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.NLLLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>The negative log likelihood loss. It is useful to train a classification
 problem with <cite>C</cite> classes.</p>
 <p>If provided, the optional argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">weight</span></code> should be a 1D Tensor assigning
diff --git a/main/generated/torch.nn.PReLU.html b/main/generated/torch.nn.PReLU.html
index 62c8908eb25..62f53036d20 100644
--- a/main/generated/torch.nn.PReLU.html
+++ b/main/generated/torch.nn.PReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PReLU<a class="headerlink" href="#prelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#PReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L1450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#PReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise PReLU function.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>PReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>a</mi><mo>∗</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{PReLU}(x) = \max(0,x) + a * \min(0,x)
diff --git a/main/generated/torch.nn.PairwiseDistance.html b/main/generated/torch.nn.PairwiseDistance.html
index 3de78810dde..689f6a05e54 100644
--- a/main/generated/torch.nn.PairwiseDistance.html
+++ b/main/generated/torch.nn.PairwiseDistance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PairwiseDistance<a class="headerlink" href="#pairwisedistance" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PairwiseDistance">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PairwiseDistance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/distance.html#PairwiseDistance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/distance.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PairwiseDistance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PairwiseDistance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/distance.html#PairwiseDistance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/distance.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PairwiseDistance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the pairwise distance between input vectors, or between columns of input matrices.</p>
 <p>Distances are computed using <code class="docutils literal notranslate"><span class="pre">p</span></code>-norm, with constant <code class="docutils literal notranslate"><span class="pre">eps</span></code> added to avoid division by zero
 if <code class="docutils literal notranslate"><span class="pre">p</span></code> is negative, i.e.:</p>
diff --git a/main/generated/torch.nn.ParameterDict.html b/main/generated/torch.nn.ParameterDict.html
index 50f562d5924..aa72563cbf2 100644
--- a/main/generated/torch.nn.ParameterDict.html
+++ b/main/generated/torch.nn.ParameterDict.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ParameterDict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L725"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ParameterDict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L725"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds parameters in a dictionary.</p>
 <p>ParameterDict can be indexed like a regular Python dictionary, but Parameters it
 contains are properly registered, and will be visible by all Module methods.
@@ -648,7 +648,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.clear">
-<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.clear" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.clear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove all items from the ParameterDict.</p>
 <dl class="field-list simple">
 </dl>
@@ -656,7 +656,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.copy">
-<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.copy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.copy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a copy of this <a class="reference internal" href="#torch.nn.ParameterDict" title="torch.nn.ParameterDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterDict</span></code></a> instance.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -667,7 +667,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.fromkeys">
-<span class="sig-name descname"><span class="pre">fromkeys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.fromkeys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L866"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.fromkeys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fromkeys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.fromkeys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L866"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.fromkeys" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a new ParameterDict with the keys provided.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -684,7 +684,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L857"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L857"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the parameter associated with key if present. Otherwise return default if provided, None if not.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -701,7 +701,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.items">
-<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L881"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.items" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L881"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.items" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ParameterDict key/value pairs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -712,7 +712,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.keys">
-<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L877"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.keys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L877"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.keys" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ParameterDict keys.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -723,7 +723,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.pop">
-<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.pop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove key from the ParameterDict and return its parameter.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -737,7 +737,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.popitem">
-<span class="sig-name descname"><span class="pre">popitem</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.popitem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L848"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.popitem" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">popitem</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.popitem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L848"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.popitem" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove and return the last inserted <cite>(key, parameter)</cite> pair from the ParameterDict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -748,7 +748,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.setdefault">
-<span class="sig-name descname"><span class="pre">setdefault</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.setdefault"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.setdefault" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">setdefault</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.setdefault"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.setdefault" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the default for a key in the Parameterdict.</p>
 <p>If key is in the ParameterDict, return its value.
 If not, insert <cite>key</cite> with a parameter <cite>default</cite> and return <cite>default</cite>.
@@ -768,7 +768,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.update">
-<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.update" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update the <a class="reference internal" href="#torch.nn.ParameterDict" title="torch.nn.ParameterDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterDict</span></code></a> with key-value pairs from <code class="docutils literal notranslate"><span class="pre">parameters</span></code>, overwriting existing keys.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -786,7 +786,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.values">
-<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.values" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ParameterDict values.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.ParameterList.html b/main/generated/torch.nn.ParameterList.html
index d8c0e0de70d..9ebbb873aca 100644
--- a/main/generated/torch.nn.ParameterList.html
+++ b/main/generated/torch.nn.ParameterList.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ParameterList<a class="headerlink" href="#parameterlist" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ParameterList">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ParameterList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ParameterList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds parameters in a list.</p>
 <p><a class="reference internal" href="#torch.nn.ParameterList" title="torch.nn.ParameterList"><code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterList</span></code></a> can be used like a regular Python
 list, but Tensors that are <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> are properly registered,
@@ -640,7 +640,7 @@ <h1>ParameterList<a class="headerlink" href="#parameterlist" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterList.append">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L668"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList.append" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L668"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList.append" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append a given value at the end of the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -654,7 +654,7 @@ <h1>ParameterList<a class="headerlink" href="#parameterlist" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterList.extend">
-<span class="sig-name descname"><span class="pre">extend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList.extend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L679"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList.extend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList.extend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L679"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList.extend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append values from a Python iterable to the end of the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.PixelShuffle.html b/main/generated/torch.nn.PixelShuffle.html
index bbaefa06725..073241623a2 100644
--- a/main/generated/torch.nn.PixelShuffle.html
+++ b/main/generated/torch.nn.PixelShuffle.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PixelShuffle<a class="headerlink" href="#pixelshuffle" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PixelShuffle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PixelShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upscale_factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pixelshuffle.html#PixelShuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pixelshuffle.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PixelShuffle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PixelShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upscale_factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pixelshuffle.html#PixelShuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pixelshuffle.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PixelShuffle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Rearrange elements in a tensor according to an upscaling factor.</p>
 <p>Rearranges elements in a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo>×</mo><msup><mi>r</mi><mn>2</mn></msup><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C \times r^2, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.0641em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8141em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>
 to a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo>×</mo><mi>r</mi><mo separator="true">,</mo><mi>W</mi><mo>×</mo><mi>r</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C, H \times r, W \times r)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8778em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mclose">)</span></span></span></span></span>, where r is an upscale factor.</p>
diff --git a/main/generated/torch.nn.PixelUnshuffle.html b/main/generated/torch.nn.PixelUnshuffle.html
index 6af6cb61b7a..3ee6033ec2f 100644
--- a/main/generated/torch.nn.PixelUnshuffle.html
+++ b/main/generated/torch.nn.PixelUnshuffle.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PixelUnshuffle<a class="headerlink" href="#pixelunshuffle" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PixelUnshuffle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PixelUnshuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">downscale_factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pixelshuffle.html#PixelUnshuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/pixelshuffle.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PixelUnshuffle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PixelUnshuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">downscale_factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pixelshuffle.html#PixelUnshuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pixelshuffle.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PixelUnshuffle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reverse the PixelShuffle operation.</p>
 <p>Reverses the <a class="reference internal" href="torch.nn.PixelShuffle.html#torch.nn.PixelShuffle" title="torch.nn.PixelShuffle"><code class="xref py py-class docutils literal notranslate"><span class="pre">PixelShuffle</span></code></a> operation by rearranging elements
 in a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo>×</mo><mi>r</mi><mo separator="true">,</mo><mi>W</mi><mo>×</mo><mi>r</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C, H \times r, W \times r)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8778em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mclose">)</span></span></span></span></span> to a tensor of shape
diff --git a/main/generated/torch.nn.PoissonNLLLoss.html b/main/generated/torch.nn.PoissonNLLLoss.html
index 10cb3392efe..427104ac3cd 100644
--- a/main/generated/torch.nn.PoissonNLLLoss.html
+++ b/main/generated/torch.nn.PoissonNLLLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PoissonNLLLoss<a class="headerlink" href="#poissonnllloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PoissonNLLLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PoissonNLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#PoissonNLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PoissonNLLLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PoissonNLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#PoissonNLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PoissonNLLLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Negative log likelihood loss with Poisson distribution of target.</p>
 <p>The loss can be described as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.RMSNorm.html b/main/generated/torch.nn.RMSNorm.html
index b7ed7bb9b4c..8c0ef55958a 100644
--- a/main/generated/torch.nn.RMSNorm.html
+++ b/main/generated/torch.nn.RMSNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RMSNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RMSNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RMSNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Root Mean Square Layer Normalization over a mini-batch of inputs.</p>
 <p>This layer implements the operation as described in
 the paper <a class="reference external" href="https://arxiv.org/pdf/1910.07467.pdf">Root Mean Square Layer Normalization</a></p>
@@ -661,7 +661,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.RMSNorm.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extra information about the module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -672,7 +672,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.RMSNorm.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs forward pass.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -683,7 +683,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.RMSNorm.reset_parameters">
-<span class="sig-name descname"><span class="pre">reset_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.reset_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.reset_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.reset_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.reset_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Resets parameters based on their initialization used in __init__.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.nn.RNN.html b/main/generated/torch.nn.RNN.html
index 90009590d4c..8caf3c6bd7d 100644
--- a/main/generated/torch.nn.RNN.html
+++ b/main/generated/torch.nn.RNN.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RNN<a class="headerlink" href="#rnn" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RNN">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNN"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/rnn.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNN" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNN"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNN" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a multi-layer Elman RNN with <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>tanh</mi><mo>⁡</mo></mrow><annotation encoding="application/x-tex">\tanh</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mop">tanh</span></span></span></span></span> or <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU</mtext></mrow><annotation encoding="application/x-tex">\text{ReLU}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord text"><span class="mord">ReLU</span></span></span></span></span></span>
 non-linearity to an input sequence. For each element in the input sequence,
 each layer computes the following function:</p>
diff --git a/main/generated/torch.nn.RNNBase.html b/main/generated/torch.nn.RNNBase.html
index f49dd8662c7..4f0f230bf16 100644
--- a/main/generated/torch.nn.RNNBase.html
+++ b/main/generated/torch.nn.RNNBase.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RNNBase<a class="headerlink" href="#rnnbase" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RNNBase">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNNBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/rnn.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNBase" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNNBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNBase" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for RNN modules (RNN, LSTM, GRU).</p>
 <p>Implements aspects of RNNs shared by the RNN, LSTM, and GRU classes, such as module initialization
 and utility methods for parameter storage management.</p>
@@ -628,7 +628,7 @@ <h1>RNNBase<a class="headerlink" href="#rnnbase" title="Permalink to this headin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.RNNBase.flatten_parameters">
-<span class="sig-name descname"><span class="pre">flatten_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNBase.flatten_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/rnn.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNBase.flatten_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">flatten_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNBase.flatten_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNBase.flatten_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset parameter data pointer so that they can use faster code paths.</p>
 <p>Right now, this works only if the module is on the GPU and cuDNN is enabled.
 Otherwise, it’s a no-op.</p>
diff --git a/main/generated/torch.nn.RNNCell.html b/main/generated/torch.nn.RNNCell.html
index ec8069e7e73..941d76fc544 100644
--- a/main/generated/torch.nn.RNNCell.html
+++ b/main/generated/torch.nn.RNNCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RNNCell<a class="headerlink" href="#rnncell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RNNCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNNCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/rnn.py#L1491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNNCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L1491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>An Elman RNN cell with tanh or ReLU non-linearity.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msup><mi>h</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>h</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>h</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>h</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>h</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">h&#x27; = \tanh(W_{ih} x + b_{ih}  +  W_{hh} h + b_{hh})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8019em;"></span><span class="mord"><span class="mord mathnormal">h</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8019em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">′</span></span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">tanh</span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">ih</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8444em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">b</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">ih</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8444em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">hh</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord mathnormal">h</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal">b</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">hh</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span></div><p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">nonlinearity</span></code> is <cite>‘relu’</cite>, then ReLU is used in place of tanh.</p>
diff --git a/main/generated/torch.nn.RReLU.html b/main/generated/torch.nn.RReLU.html
index 18edd80cd58..7708816a39a 100644
--- a/main/generated/torch.nn.RReLU.html
+++ b/main/generated/torch.nn.RReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RReLU<a class="headerlink" href="#rrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#RReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#RReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the randomized leaky rectified linear unit function, element-wise.</p>
 <p>Method described in the paper:
 <a class="reference external" href="https://arxiv.org/abs/1505.00853">Empirical Evaluation of Rectified Activations in Convolutional Network</a>.</p>
diff --git a/main/generated/torch.nn.ReLU.html b/main/generated/torch.nn.ReLU.html
index 44260696335..ac92da75688 100644
--- a/main/generated/torch.nn.ReLU.html
+++ b/main/generated/torch.nn.ReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReLU<a class="headerlink" href="#relu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the rectified linear unit function element-wise.</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mo stretchy="false">(</mo><mi>x</mi><msup><mo stretchy="false">)</mo><mo>+</mo></msup><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU}(x) = (x)^+ = \max(0, x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">ReLU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0213em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose"><span class="mclose">)</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7713em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mbin mtight">+</span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span></p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReLU6.html b/main/generated/torch.nn.ReLU6.html
index a063938a981..089ceb9f1e5 100644
--- a/main/generated/torch.nn.ReLU6.html
+++ b/main/generated/torch.nn.ReLU6.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReLU6<a class="headerlink" href="#relu6" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReLU6">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReLU6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ReLU6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReLU6" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReLU6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ReLU6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReLU6" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the ReLU6 function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>ReLU6</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo separator="true">,</mo><mn>6</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU6}(x) = \min(\max(0,x), 6)
diff --git a/main/generated/torch.nn.ReflectionPad1d.html b/main/generated/torch.nn.ReflectionPad1d.html
index daf4def073b..94ff268625d 100644
--- a/main/generated/torch.nn.ReflectionPad1d.html
+++ b/main/generated/torch.nn.ReflectionPad1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReflectionPad1d<a class="headerlink" href="#reflectionpad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReflectionPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using the reflection of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReflectionPad2d.html b/main/generated/torch.nn.ReflectionPad2d.html
index 4cb7cd9f0ad..ae728e50166 100644
--- a/main/generated/torch.nn.ReflectionPad2d.html
+++ b/main/generated/torch.nn.ReflectionPad2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReflectionPad2d<a class="headerlink" href="#reflectionpad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReflectionPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using the reflection of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReflectionPad3d.html b/main/generated/torch.nn.ReflectionPad3d.html
index c8657a662f0..2770742ca21 100644
--- a/main/generated/torch.nn.ReflectionPad3d.html
+++ b/main/generated/torch.nn.ReflectionPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReflectionPad3d<a class="headerlink" href="#reflectionpad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReflectionPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using the reflection of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReplicationPad1d.html b/main/generated/torch.nn.ReplicationPad1d.html
index 5b3c448204e..9eea53fceae 100644
--- a/main/generated/torch.nn.ReplicationPad1d.html
+++ b/main/generated/torch.nn.ReplicationPad1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReplicationPad1d<a class="headerlink" href="#replicationpad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReplicationPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using replication of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReplicationPad2d.html b/main/generated/torch.nn.ReplicationPad2d.html
index 01ec347ca06..cadaf736e54 100644
--- a/main/generated/torch.nn.ReplicationPad2d.html
+++ b/main/generated/torch.nn.ReplicationPad2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReplicationPad2d<a class="headerlink" href="#replicationpad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReplicationPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using replication of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReplicationPad3d.html b/main/generated/torch.nn.ReplicationPad3d.html
index c159ee5e4ff..f8c4f464ca2 100644
--- a/main/generated/torch.nn.ReplicationPad3d.html
+++ b/main/generated/torch.nn.ReplicationPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReplicationPad3d<a class="headerlink" href="#replicationpad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReplicationPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using replication of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.SELU.html b/main/generated/torch.nn.SELU.html
index 0533d3ae582..2ed86706381 100644
--- a/main/generated/torch.nn.SELU.html
+++ b/main/generated/torch.nn.SELU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SELU<a class="headerlink" href="#selu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#SELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#SELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the SELU function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>SELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mtext>scale</mtext><mo>∗</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{SELU}(x) = \text{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))
diff --git a/main/generated/torch.nn.Sequential.html b/main/generated/torch.nn.Sequential.html
index 49a9c9c40a4..f2e903086a4 100644
--- a/main/generated/torch.nn.Sequential.html
+++ b/main/generated/torch.nn.Sequential.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Sequential<a class="headerlink" href="#sequential" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Sequential">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><span class="pre">Module</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#Sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sequential" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><span class="pre">Module</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#Sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sequential" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/collections.html#collections.OrderedDict" title="(in Python v3.13)"><span class="pre">OrderedDict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><span class="pre">Module</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span></dt>
 <dd><p>A sequential container.</p>
@@ -657,7 +657,7 @@ <h1>Sequential<a class="headerlink" href="#sequential" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Sequential.append">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#Sequential.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/container.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sequential.append" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#Sequential.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sequential.append" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append a given module to the end.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.SiLU.html b/main/generated/torch.nn.SiLU.html
index 40382c440f9..6d94765266e 100644
--- a/main/generated/torch.nn.SiLU.html
+++ b/main/generated/torch.nn.SiLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SiLU<a class="headerlink" href="#silu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SiLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SiLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#SiLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L395"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SiLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SiLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#SiLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L395"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SiLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Sigmoid Linear Unit (SiLU) function, element-wise.</p>
 <p>The SiLU function is also known as the swish function.</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Sigmoid.html b/main/generated/torch.nn.Sigmoid.html
index 2d25806631c..eadcd814f16 100644
--- a/main/generated/torch.nn.Sigmoid.html
+++ b/main/generated/torch.nn.Sigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Sigmoid<a class="headerlink" href="#sigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Sigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L306"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L306"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Sigmoid function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>Sigmoid</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}
diff --git a/main/generated/torch.nn.SmoothL1Loss.html b/main/generated/torch.nn.SmoothL1Loss.html
index 4e7a9effb08..1892f375bc5 100644
--- a/main/generated/torch.nn.SmoothL1Loss.html
+++ b/main/generated/torch.nn.SmoothL1Loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SmoothL1Loss<a class="headerlink" href="#smoothl1loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SmoothL1Loss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SmoothL1Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#SmoothL1Loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SmoothL1Loss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SmoothL1Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#SmoothL1Loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SmoothL1Loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that uses a squared term if the absolute
 element-wise error falls below beta and an L1 term otherwise.
 It is less sensitive to outliers than <a class="reference internal" href="torch.nn.MSELoss.html#torch.nn.MSELoss" title="torch.nn.MSELoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.MSELoss</span></code></a> and in some cases
diff --git a/main/generated/torch.nn.SoftMarginLoss.html b/main/generated/torch.nn.SoftMarginLoss.html
index bf7d37cc7f6..ab999414940 100644
--- a/main/generated/torch.nn.SoftMarginLoss.html
+++ b/main/generated/torch.nn.SoftMarginLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SoftMarginLoss<a class="headerlink" href="#softmarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SoftMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SoftMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#SoftMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SoftMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SoftMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#SoftMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SoftMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that optimizes a two-class classification
 logistic loss between input tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and target tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span>
 (containing 1 or -1).</p>
diff --git a/main/generated/torch.nn.Softmax.html b/main/generated/torch.nn.Softmax.html
index 74a165984fa..f69a4f73f27 100644
--- a/main/generated/torch.nn.Softmax.html
+++ b/main/generated/torch.nn.Softmax.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softmax<a class="headerlink" href="#softmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softmax">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L1619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmax" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Softmax function to an n-dimensional input Tensor.</p>
 <p>Rescales them so that the elements of the n-dimensional output Tensor
 lie in the range [0,1] and sum to 1.</p>
diff --git a/main/generated/torch.nn.Softmax2d.html b/main/generated/torch.nn.Softmax2d.html
index ca7c1e501c6..70f4cbfb856 100644
--- a/main/generated/torch.nn.Softmax2d.html
+++ b/main/generated/torch.nn.Softmax2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softmax2d<a class="headerlink" href="#softmax2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softmax2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmax2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmax2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmax2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmax2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmax2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmax2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies SoftMax over features to each spatial location.</p>
 <p>When given an image of <code class="docutils literal notranslate"><span class="pre">Channels</span> <span class="pre">x</span> <span class="pre">Height</span> <span class="pre">x</span> <span class="pre">Width</span></code>, it will
 apply <cite>Softmax</cite> to each location <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>C</mi><mi>h</mi><mi>a</mi><mi>n</mi><mi>n</mi><mi>e</mi><mi>l</mi><mi>s</mi><mo separator="true">,</mo><msub><mi>h</mi><mi>i</mi></msub><mo separator="true">,</mo><msub><mi>w</mi><mi>j</mi></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(Channels, h_i, w_j)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.0361em;vertical-align:-0.2861em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mord mathnormal">hann</span><span class="mord mathnormal">e</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mord mathnormal">s</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">h</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02691em;">w</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.0269em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.05724em;">j</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span></p>
diff --git a/main/generated/torch.nn.Softmin.html b/main/generated/torch.nn.Softmin.html
index b9b8f63e6e3..d73f4e20482 100644
--- a/main/generated/torch.nn.Softmin.html
+++ b/main/generated/torch.nn.Softmin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softmin<a class="headerlink" href="#softmin" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softmin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L1569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmin" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Softmin function to an n-dimensional input Tensor.</p>
 <p>Rescales them so that the elements of the n-dimensional output Tensor
 lie in the range <cite>[0, 1]</cite> and sum to 1.</p>
diff --git a/main/generated/torch.nn.Softplus.html b/main/generated/torch.nn.Softplus.html
index dc757b13cb1..bbaff469f86 100644
--- a/main/generated/torch.nn.Softplus.html
+++ b/main/generated/torch.nn.Softplus.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softplus<a class="headerlink" href="#softplus" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softplus">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softplus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softplus"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L858"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softplus" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softplus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softplus"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L858"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softplus" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Softplus function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>Softplus</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mn>1</mn><mi>β</mi></mfrac><mo>∗</mo><mi>log</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>β</mi><mo>∗</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
diff --git a/main/generated/torch.nn.Softshrink.html b/main/generated/torch.nn.Softshrink.html
index b8e68abf8fc..d7f27bc6a40 100644
--- a/main/generated/torch.nn.Softshrink.html
+++ b/main/generated/torch.nn.Softshrink.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softshrink<a class="headerlink" href="#softshrink" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softshrink">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softshrink" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softshrink" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the soft shrinkage function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>SoftShrinkage</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mrow><mo fence="true">{</mo><mtable rowspacing="0.36em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>x</mi><mo>−</mo><mi>λ</mi><mo separator="true">,</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext> if </mtext><mi>x</mi><mo>&gt;</mo><mi>λ</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>x</mi><mo>+</mo><mi>λ</mi><mo separator="true">,</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext> if </mtext><mi>x</mi><mo>&lt;</mo><mo>−</mo><mi>λ</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mn>0</mn><mo separator="true">,</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mtext> otherwise </mtext></mstyle></mtd></mtr></mtable></mrow></mrow><annotation encoding="application/x-tex">\text{SoftShrinkage}(x) =
diff --git a/main/generated/torch.nn.Softsign.html b/main/generated/torch.nn.Softsign.html
index 9b899cc8910..cf1bed769fd 100644
--- a/main/generated/torch.nn.Softsign.html
+++ b/main/generated/torch.nn.Softsign.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softsign<a class="headerlink" href="#softsign" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softsign">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softsign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softsign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L1523"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softsign" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softsign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softsign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1523"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softsign" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise Softsign function.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>SoftSign</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mi>x</mi><mrow><mn>1</mn><mo>+</mo><mi mathvariant="normal">∣</mi><mi>x</mi><mi mathvariant="normal">∣</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{SoftSign}(x) = \frac{x}{ 1 + |x|}
diff --git a/main/generated/torch.nn.SyncBatchNorm.html b/main/generated/torch.nn.SyncBatchNorm.html
index cb351e86a1b..0e6ff6e49e2 100644
--- a/main/generated/torch.nn.SyncBatchNorm.html
+++ b/main/generated/torch.nn.SyncBatchNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SyncBatchNorm<a class="headerlink" href="#syncbatchnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SyncBatchNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SyncBatchNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#SyncBatchNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SyncBatchNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SyncBatchNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#SyncBatchNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SyncBatchNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Batch Normalization over a N-Dimensional input.</p>
 <p>The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
 <a class="reference external" href="https://arxiv.org/abs/1502.03167">Batch Normalization: Accelerating Deep Network Training by Reducing
@@ -724,7 +724,7 @@ <h1>SyncBatchNorm<a class="headerlink" href="#syncbatchnorm" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.SyncBatchNorm.convert_sync_batchnorm">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">convert_sync_batchnorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#SyncBatchNorm.convert_sync_batchnorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/batchnorm.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SyncBatchNorm.convert_sync_batchnorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">convert_sync_batchnorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#SyncBatchNorm.convert_sync_batchnorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SyncBatchNorm.convert_sync_batchnorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts all <code class="xref py py-attr docutils literal notranslate"><span class="pre">BatchNorm*D</span></code> layers in the model to <a class="reference internal" href="#torch.nn.SyncBatchNorm" title="torch.nn.SyncBatchNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.SyncBatchNorm</span></code></a> layers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.Tanh.html b/main/generated/torch.nn.Tanh.html
index c187eb66863..18cdc80c5c6 100644
--- a/main/generated/torch.nn.Tanh.html
+++ b/main/generated/torch.nn.Tanh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Tanh<a class="headerlink" href="#tanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Tanh">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Tanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Tanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Tanh" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Tanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Tanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Tanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Hyperbolic Tangent (Tanh) function element-wise.</p>
 <p>Tanh is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Tanhshrink.html b/main/generated/torch.nn.Tanhshrink.html
index abebeb614c9..c5b683441c4 100644
--- a/main/generated/torch.nn.Tanhshrink.html
+++ b/main/generated/torch.nn.Tanhshrink.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Tanhshrink<a class="headerlink" href="#tanhshrink" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Tanhshrink">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Tanhshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Tanhshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L1546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Tanhshrink" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Tanhshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Tanhshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Tanhshrink" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise Tanhshrink function.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>Tanhshrink</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mo>−</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Tanhshrink}(x) = x - \tanh(x)
diff --git a/main/generated/torch.nn.Threshold.html b/main/generated/torch.nn.Threshold.html
index f5c5a89aef4..ed4da41c359 100644
--- a/main/generated/torch.nn.Threshold.html
+++ b/main/generated/torch.nn.Threshold.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Threshold<a class="headerlink" href="#threshold" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Threshold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/activation.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Threshold" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Threshold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Thresholds each element of the input Tensor.</p>
 <p>Threshold is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Transformer.html b/main/generated/torch.nn.Transformer.html
index 667dcd27830..0d6d01aca13 100644
--- a/main/generated/torch.nn.Transformer.html
+++ b/main/generated/torch.nn.Transformer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Transformer<a class="headerlink" href="#transformer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Transformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model=512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead=8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_encoder_layers=6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_decoder_layers=6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_encoder=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_decoder=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model=512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead=8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_encoder_layers=6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_decoder_layers=6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_encoder=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_decoder=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer" title="Permalink to this definition">¶</a></dt>
 <dd><p>A transformer model.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -658,7 +658,7 @@ <h1>Transformer<a class="headerlink" href="#transformer" title="Permalink to thi
 <a class="reference external" href="https://github.com/pytorch/examples/tree/master/word_language_model">https://github.com/pytorch/examples/tree/master/word_language_model</a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Transformer.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Take in and process masked source/target sequences.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -745,7 +745,7 @@ <h1>Transformer<a class="headerlink" href="#transformer" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Transformer.generate_square_subsequent_mask">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">generate_square_subsequent_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer.generate_square_subsequent_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer.generate_square_subsequent_mask" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">generate_square_subsequent_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer.generate_square_subsequent_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer.generate_square_subsequent_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generate a square causal mask for the sequence.</p>
 <p>The masked positions are filled with float(‘-inf’). Unmasked positions are filled with float(0.0).</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.TransformerDecoder.html b/main/generated/torch.nn.TransformerDecoder.html
index 075e4b272f0..82050c03889 100644
--- a/main/generated/torch.nn.TransformerDecoder.html
+++ b/main/generated/torch.nn.TransformerDecoder.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>TransformerDecoder<a class="headerlink" href="#transformerdecoder" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TransformerDecoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerDecoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decoder_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoder" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerDecoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decoder_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoder" title="Permalink to this definition">¶</a></dt>
 <dd><p>TransformerDecoder is a stack of N decoder layers.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -641,7 +641,7 @@ <h1>TransformerDecoder<a class="headerlink" href="#transformerdecoder" title="Pe
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.TransformerDecoder.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoder.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L565"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoder.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoder.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L565"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoder.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pass the inputs (and mask) through the decoder layer in turn.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.TransformerDecoderLayer.html b/main/generated/torch.nn.TransformerDecoderLayer.html
index 234b1e897a9..43167c75da6 100644
--- a/main/generated/torch.nn.TransformerDecoderLayer.html
+++ b/main/generated/torch.nn.TransformerDecoderLayer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>TransformerDecoderLayer<a class="headerlink" href="#transformerdecoderlayer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TransformerDecoderLayer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerDecoderLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoderLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L945"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoderLayer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerDecoderLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoderLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L945"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoderLayer" title="Permalink to this definition">¶</a></dt>
 <dd><p>TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -660,7 +660,7 @@ <h1>TransformerDecoderLayer<a class="headerlink" href="#transformerdecoderlayer"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.TransformerDecoderLayer.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoderLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoderLayer.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoderLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoderLayer.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pass the inputs (and mask) through the decoder layer.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.TransformerEncoder.html b/main/generated/torch.nn.TransformerEncoder.html
index 6b348d69cf0..9563d8fd87e 100644
--- a/main/generated/torch.nn.TransformerEncoder.html
+++ b/main/generated/torch.nn.TransformerEncoder.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>TransformerEncoder<a class="headerlink" href="#transformerencoder" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TransformerEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">encoder_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_nested_tensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoder" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">encoder_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_nested_tensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoder" title="Permalink to this definition">¶</a></dt>
 <dd><p>TransformerEncoder is a stack of N encoder layers.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -644,7 +644,7 @@ <h1>TransformerEncoder<a class="headerlink" href="#transformerencoder" title="Pe
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.TransformerEncoder.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoder.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoder.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoder.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoder.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pass the input through the encoder layers in turn.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.TransformerEncoderLayer.html b/main/generated/torch.nn.TransformerEncoderLayer.html
index 69d787c0669..0d8315023cd 100644
--- a/main/generated/torch.nn.TransformerEncoderLayer.html
+++ b/main/generated/torch.nn.TransformerEncoderLayer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>TransformerEncoderLayer<a class="headerlink" href="#transformerencoderlayer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TransformerEncoderLayer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerEncoderLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoderLayer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerEncoderLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoderLayer" title="Permalink to this definition">¶</a></dt>
 <dd><p>TransformerEncoderLayer is made up of self-attn and feedforward network.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -690,7 +690,7 @@ <h1>TransformerEncoderLayer<a class="headerlink" href="#transformerencoderlayer"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.TransformerEncoderLayer.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/transformer.py#L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoderLayer.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoderLayer.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pass the input through the encoder layer.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.TripletMarginLoss.html b/main/generated/torch.nn.TripletMarginLoss.html
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@@ -612,7 +612,7 @@
 <h1>TripletMarginLoss<a class="headerlink" href="#tripletmarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TripletMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TripletMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#TripletMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1589"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TripletMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TripletMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#TripletMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1589"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TripletMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the triplet loss given an input
 tensors <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>1</mn></mrow><annotation encoding="application/x-tex">x1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">1</span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>2</mn></mrow><annotation encoding="application/x-tex">x2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">2</span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>3</mn></mrow><annotation encoding="application/x-tex">x3</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">3</span></span></span></span></span> and a margin with a value greater than <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>0</mn></mrow><annotation encoding="application/x-tex">0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">0</span></span></span></span></span>.
 This is used for measuring a relative similarity between samples. A triplet
diff --git a/main/generated/torch.nn.TripletMarginWithDistanceLoss.html b/main/generated/torch.nn.TripletMarginWithDistanceLoss.html
index 36594662ff0..d111400610f 100644
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@@ -300,7 +300,7 @@
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     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>TripletMarginWithDistanceLoss<a class="headerlink" href="#tripletmarginwithdistanceloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TripletMarginWithDistanceLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TripletMarginWithDistanceLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#TripletMarginWithDistanceLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/loss.py#L1697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TripletMarginWithDistanceLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TripletMarginWithDistanceLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#TripletMarginWithDistanceLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TripletMarginWithDistanceLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the triplet loss given input
 tensors <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>a</mi></mrow><annotation encoding="application/x-tex">a</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">a</span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi></mrow><annotation encoding="application/x-tex">p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal">p</span></span></span></span></span>, and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi></mrow><annotation encoding="application/x-tex">n</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">n</span></span></span></span></span> (representing anchor,
 positive, and negative examples, respectively), and a nonnegative,
diff --git a/main/generated/torch.nn.Unflatten.html b/main/generated/torch.nn.Unflatten.html
index fc58429c3fb..c07c43fdb44 100644
--- a/main/generated/torch.nn.Unflatten.html
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           <div class="pytorch-left-menu-search">
             
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Unflatten<a class="headerlink" href="#unflatten" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Unflatten">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unflattened_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/flatten.html#Unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/flatten.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Unflatten" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unflattened_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/flatten.html#Unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/flatten.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Unflatten" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unflattens a tensor dim expanding it to a desired shape. For use with <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code>.</p>
 <ul class="simple">
 <li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> specifies the dimension of the input tensor to be unflattened, and it can
diff --git a/main/generated/torch.nn.Unfold.html b/main/generated/torch.nn.Unfold.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Unfold<a class="headerlink" href="#unfold" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Unfold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Unfold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/fold.html#Unfold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/fold.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Unfold" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Unfold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/fold.html#Unfold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/fold.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Unfold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extracts sliding local blocks from a batched input tensor.</p>
 <p>Consider a batched <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mo>∗</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, *)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">∗</span><span class="mclose">)</span></span></span></span></span>,
 where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi></mrow><annotation encoding="application/x-tex">N</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span></span></span></span></span> is the batch dimension, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>C</mi></mrow><annotation encoding="application/x-tex">C</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span></span></span></span></span> is the channel dimension,
diff --git a/main/generated/torch.nn.Upsample.html b/main/generated/torch.nn.Upsample.html
index 48f7a52d7c4..b65f4c48d55 100644
--- a/main/generated/torch.nn.Upsample.html
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Upsample<a class="headerlink" href="#upsample" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Upsample">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompute_scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#Upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/upsampling.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Upsample" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompute_scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#Upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/upsampling.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Upsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.</p>
 <p>The input data is assumed to be of the form
 <cite>minibatch x channels x [optional depth] x [optional height] x width</cite>.
diff --git a/main/generated/torch.nn.UpsamplingBilinear2d.html b/main/generated/torch.nn.UpsamplingBilinear2d.html
index a48bed04617..91a001f5fc9 100644
--- a/main/generated/torch.nn.UpsamplingBilinear2d.html
+++ b/main/generated/torch.nn.UpsamplingBilinear2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UpsamplingBilinear2d<a class="headerlink" href="#upsamplingbilinear2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.UpsamplingBilinear2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">UpsamplingBilinear2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#UpsamplingBilinear2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/upsampling.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.UpsamplingBilinear2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">UpsamplingBilinear2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#UpsamplingBilinear2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/upsampling.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.UpsamplingBilinear2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D bilinear upsampling to an input signal composed of several input channels.</p>
 <p>To specify the scale, it takes either the <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code>
 as it’s constructor argument.</p>
diff --git a/main/generated/torch.nn.UpsamplingNearest2d.html b/main/generated/torch.nn.UpsamplingNearest2d.html
index 0079bb462f6..3242464f88a 100644
--- a/main/generated/torch.nn.UpsamplingNearest2d.html
+++ b/main/generated/torch.nn.UpsamplingNearest2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UpsamplingNearest2d<a class="headerlink" href="#upsamplingnearest2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.UpsamplingNearest2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">UpsamplingNearest2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#UpsamplingNearest2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/upsampling.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.UpsamplingNearest2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">UpsamplingNearest2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#UpsamplingNearest2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/upsampling.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.UpsamplingNearest2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D nearest neighbor upsampling to an input signal composed of several input channels.</p>
 <p>To specify the scale, it takes either the <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code>
 as it’s constructor argument.</p>
diff --git a/main/generated/torch.nn.ZeroPad1d.html b/main/generated/torch.nn.ZeroPad1d.html
index 931cb31bb60..46fcd4aa8cf 100644
--- a/main/generated/torch.nn.ZeroPad1d.html
+++ b/main/generated/torch.nn.ZeroPad1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ZeroPad1d<a class="headerlink" href="#zeropad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ZeroPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L667"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L667"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with zero.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ZeroPad2d.html b/main/generated/torch.nn.ZeroPad2d.html
index 5ef7244061c..794714e03d6 100644
--- a/main/generated/torch.nn.ZeroPad2d.html
+++ b/main/generated/torch.nn.ZeroPad2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ZeroPad2d<a class="headerlink" href="#zeropad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ZeroPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with zero.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ZeroPad3d.html b/main/generated/torch.nn.ZeroPad3d.html
index 94705762780..c207568139f 100644
--- a/main/generated/torch.nn.ZeroPad3d.html
+++ b/main/generated/torch.nn.ZeroPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ZeroPad3d<a class="headerlink" href="#zeropad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ZeroPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/padding.py#L774"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L774"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with zero.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.attention.SDPBackend.html b/main/generated/torch.nn.attention.SDPBackend.html
index 1229ae6a7de..05c78e1612f 100644
--- a/main/generated/torch.nn.attention.SDPBackend.html
+++ b/main/generated/torch.nn.attention.SDPBackend.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.nn.attention.bias.CausalBias.html b/main/generated/torch.nn.attention.bias.CausalBias.html
index 6cb1c9e9c47..5a2e9765302 100644
--- a/main/generated/torch.nn.attention.bias.CausalBias.html
+++ b/main/generated/torch.nn.attention.bias.CausalBias.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.nn.attention.bias.CausalBias<a class="headerlink" href="#torch-nn-attention-bias-causalbias" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.attention.bias.CausalBias">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">CausalBias</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">variant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_len_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_len_kv</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#CausalBias"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/bias.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.CausalBias" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">CausalBias</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">variant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_len_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_len_kv</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#CausalBias"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/bias.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.CausalBias" title="Permalink to this definition">¶</a></dt>
 <dd><p>A bias representing causal attention patterns. For an overview of the bias structure, see the <a class="reference internal" href="torch.nn.attention.bias.CausalVariant.html#torch.nn.attention.bias.CausalVariant" title="torch.nn.attention.bias.CausalVariant"><code class="xref py py-class docutils literal notranslate"><span class="pre">CausalVariant</span></code></a> enum.</p>
 <p>This class is used for defining causal (triangular) attention biases. For construing the bias, there exist
 two factory functions: <a class="reference internal" href="torch.nn.attention.bias.causal_upper_left.html#torch.nn.attention.bias.causal_upper_left" title="torch.nn.attention.bias.causal_upper_left"><code class="xref py py-func docutils literal notranslate"><span class="pre">causal_upper_left()</span></code></a> and <a class="reference internal" href="torch.nn.attention.bias.causal_lower_right.html#torch.nn.attention.bias.causal_lower_right" title="torch.nn.attention.bias.causal_lower_right"><code class="xref py py-func docutils literal notranslate"><span class="pre">causal_lower_right()</span></code></a>.</p>
diff --git a/main/generated/torch.nn.attention.bias.CausalVariant.html b/main/generated/torch.nn.attention.bias.CausalVariant.html
index 724020d367f..ea600092237 100644
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 <h1>CausalVariant<a class="headerlink" href="#causalvariant" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.attention.bias.CausalVariant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">CausalVariant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#CausalVariant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/bias.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.CausalVariant" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">CausalVariant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#CausalVariant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/bias.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.CausalVariant" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enum for causal variants used in attention mechanisms.</p>
 <p>Defines two types of causal biases:</p>
 <p><cite>UPPER_LEFT</cite>: Represents upper-left triangular bias for standard causal attention.
diff --git a/main/generated/torch.nn.attention.bias.causal_lower_right.html b/main/generated/torch.nn.attention.bias.causal_lower_right.html
index a9ae844b00d..f74f74f4271 100644
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 <h1>torch.nn.attention.bias.causal_lower_right<a class="headerlink" href="#torch-nn-attention-bias-causal-lower-right" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.bias.causal_lower_right">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">causal_lower_right</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#causal_lower_right"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/bias.py#L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.causal_lower_right" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">causal_lower_right</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#causal_lower_right"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/bias.py#L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.causal_lower_right" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a lower-right triangular causal bias.</p>
 <p>This function generates a lower-right triangular matrix to represent causal attention bias with a
 diagonal offset set so that the inclusive values are aligned to the lower right corner of the matrix.</p>
diff --git a/main/generated/torch.nn.attention.bias.causal_upper_left.html b/main/generated/torch.nn.attention.bias.causal_upper_left.html
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 <h1>torch.nn.attention.bias.causal_upper_left<a class="headerlink" href="#torch-nn-attention-bias-causal-upper-left" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.bias.causal_upper_left">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">causal_upper_left</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#causal_upper_left"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/bias.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.causal_upper_left" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">causal_upper_left</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#causal_upper_left"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/bias.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.causal_upper_left" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates an upper-left triangular causal bias.</p>
 <p>This function generates a upper-left triangular matrix to represent causal attention bias with a
 diagonal offset set so that the inclusive values are aligned to the upper left corner of the matrix.
diff --git a/main/generated/torch.nn.attention.sdpa_kernel.html b/main/generated/torch.nn.attention.sdpa_kernel.html
index 879e6c0f153..80860f792ba 100644
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 <h1>torch.nn.attention.sdpa_kernel<a class="headerlink" href="#torch-nn-attention-sdpa-kernel" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.sdpa_kernel">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.</span></span><span class="sig-name descname"><span class="pre">sdpa_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backends</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">set_priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention.html#sdpa_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/__init__.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.sdpa_kernel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.</span></span><span class="sig-name descname"><span class="pre">sdpa_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backends</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">set_priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention.html#sdpa_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/__init__.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.sdpa_kernel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager to select which backend to use for scaled dot product attention.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.functional.adaptive_avg_pool1d.html b/main/generated/torch.nn.functional.adaptive_avg_pool1d.html
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 <h1>torch.nn.functional.adaptive_avg_pool2d<a class="headerlink" href="#torch-nn-functional-adaptive-avg-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_avg_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#adaptive_avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#adaptive_avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 2D adaptive average pooling over an input signal composed of several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveAvgPool2d.html#torch.nn.AdaptiveAvgPool2d" title="torch.nn.AdaptiveAvgPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveAvgPool2d</span></code></a> for details and output shape.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.adaptive_avg_pool3d.html b/main/generated/torch.nn.functional.adaptive_avg_pool3d.html
index 98832baae07..2422716de9d 100644
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 <h1>torch.nn.functional.adaptive_avg_pool3d<a class="headerlink" href="#torch-nn-functional-adaptive-avg-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_avg_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#adaptive_avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#adaptive_avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 3D adaptive average pooling over an input signal composed of several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveAvgPool3d.html#torch.nn.AdaptiveAvgPool3d" title="torch.nn.AdaptiveAvgPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveAvgPool3d</span></code></a> for details and output shape.</p>
 <dl class="field-list simple">
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 <h1>torch.nn.functional.adaptive_max_pool1d<a class="headerlink" href="#torch-nn-functional-adaptive-max-pool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_max_pool1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D adaptive max pooling over an input signal composed of
 several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveMaxPool1d.html#torch.nn.AdaptiveMaxPool1d" title="torch.nn.AdaptiveMaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveMaxPool1d</span></code></a> for details and output shape.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.adaptive_max_pool2d<a class="headerlink" href="#torch-nn-functional-adaptive-max-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_max_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D adaptive max pooling over an input signal composed of
 several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveMaxPool2d.html#torch.nn.AdaptiveMaxPool2d" title="torch.nn.AdaptiveMaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveMaxPool2d</span></code></a> for details and output shape.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.adaptive_max_pool3d<a class="headerlink" href="#torch-nn-functional-adaptive-max-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_max_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D adaptive max pooling over an input signal composed of
 several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveMaxPool3d.html#torch.nn.AdaptiveMaxPool3d" title="torch.nn.AdaptiveMaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveMaxPool3d</span></code></a> for details and output shape.</p>
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 <h1>torch.nn.functional.affine_grid<a class="headerlink" href="#torch-nn-functional-affine-grid" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.affine_grid">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">affine_grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#affine_grid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L5026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.affine_grid" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">affine_grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#affine_grid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.affine_grid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generate 2D or 3D flow field (sampling grid), given a batch of affine matrices <code class="xref py py-attr docutils literal notranslate"><span class="pre">theta</span></code>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.nn.functional.alpha_dropout.html b/main/generated/torch.nn.functional.alpha_dropout.html
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 <h1>torch.nn.functional.alpha_dropout<a class="headerlink" href="#torch-nn-functional-alpha-dropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.alpha_dropout">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">alpha_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#alpha_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.alpha_dropout" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">alpha_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#alpha_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.alpha_dropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply alpha dropout to the input.</p>
 <p>See <a class="reference internal" href="torch.nn.AlphaDropout.html#torch.nn.AlphaDropout" title="torch.nn.AlphaDropout"><code class="xref py py-class docutils literal notranslate"><span class="pre">AlphaDropout</span></code></a> for details.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.batch_norm<a class="headerlink" href="#torch-nn-functional-batch-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.batch_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">batch_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#batch_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.batch_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">batch_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#batch_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.batch_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Batch Normalization for each channel across a batch of data.</p>
 <p>See <a class="reference internal" href="torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d" title="torch.nn.BatchNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm1d</span></code></a>, <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a>,
 <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm3d</span></code></a> for details.</p>
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diff --git a/main/generated/torch.nn.functional.binary_cross_entropy.html b/main/generated/torch.nn.functional.binary_cross_entropy.html
index 72e620d79e9..4feca16632c 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.binary_cross_entropy<a class="headerlink" href="#torch-nn-functional-binary-cross-entropy" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.binary_cross_entropy">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">binary_cross_entropy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#binary_cross_entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.binary_cross_entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">binary_cross_entropy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#binary_cross_entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.binary_cross_entropy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Measure Binary Cross Entropy between the target and input probabilities.</p>
 <p>See <a class="reference internal" href="torch.nn.BCELoss.html#torch.nn.BCELoss" title="torch.nn.BCELoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCELoss</span></code></a> for details.</p>
 <dl class="field-list simple">
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 <h1>torch.nn.functional.binary_cross_entropy_with_logits<a class="headerlink" href="#torch-nn-functional-binary-cross-entropy-with-logits" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.binary_cross_entropy_with_logits">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">binary_cross_entropy_with_logits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#binary_cross_entropy_with_logits"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.binary_cross_entropy_with_logits" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">binary_cross_entropy_with_logits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#binary_cross_entropy_with_logits"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.binary_cross_entropy_with_logits" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate Binary Cross Entropy between target and input logits.</p>
 <p>See <a class="reference internal" href="torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="torch.nn.BCEWithLogitsLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.celu.html b/main/generated/torch.nn.functional.celu.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.celu<a class="headerlink" href="#torch-nn-functional-celu" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">celu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#celu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1849"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.celu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">celu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#celu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1849"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.celu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise,
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>CELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mi mathvariant="normal">/</mi><mi>α</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">CELU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mord">/</span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">1</span><span class="mclose">))</span></span></span></span></span>.</p>
 <p>See <a class="reference internal" href="torch.nn.CELU.html#torch.nn.CELU" title="torch.nn.CELU"><code class="xref py py-class docutils literal notranslate"><span class="pre">CELU</span></code></a> for more details.</p>
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 <h1>torch.nn.functional.cosine_embedding_loss<a class="headerlink" href="#torch-nn-functional-cosine-embedding-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.cosine_embedding_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">cosine_embedding_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#cosine_embedding_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L4081"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.cosine_embedding_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">cosine_embedding_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#cosine_embedding_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4081"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.cosine_embedding_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.CosineEmbeddingLoss.html#torch.nn.CosineEmbeddingLoss" title="torch.nn.CosineEmbeddingLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">CosineEmbeddingLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
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diff --git a/main/generated/torch.nn.functional.cross_entropy.html b/main/generated/torch.nn.functional.cross_entropy.html
index 507805bb157..94355d51b97 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.cross_entropy<a class="headerlink" href="#torch-nn-functional-cross-entropy" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.cross_entropy">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">cross_entropy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#cross_entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.cross_entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">cross_entropy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#cross_entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.cross_entropy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the cross entropy loss between input logits and target.</p>
 <p>See <a class="reference internal" href="torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.ctc_loss.html b/main/generated/torch.nn.functional.ctc_loss.html
index 44e21fcf458..ebe10de825d 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.ctc_loss<a class="headerlink" href="#torch-nn-functional-ctc-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.ctc_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">ctc_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_probs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_infinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#ctc_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.ctc_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">ctc_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_probs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_infinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#ctc_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.ctc_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Connectionist Temporal Classification loss.</p>
 <p>See <a class="reference internal" href="torch.nn.CTCLoss.html#torch.nn.CTCLoss" title="torch.nn.CTCLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">CTCLoss</span></code></a> for details.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.nn.functional.dropout.html b/main/generated/torch.nn.functional.dropout.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.dropout<a class="headerlink" href="#torch-nn-functional-dropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.dropout">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>During training, randomly zeroes some elements of the input tensor with probability <code class="xref py py-attr docutils literal notranslate"><span class="pre">p</span></code>.</p>
 <p>Uses samples from a Bernoulli distribution.</p>
 <p>See <a class="reference internal" href="torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dropout</span></code></a> for details.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.dropout1d<a class="headerlink" href="#torch-nn-functional-dropout1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.dropout1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels (a channel is a 1D feature map).</p>
 <p>For example, the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
 batched input is a 1D tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo stretchy="false">[</mo><mi>i</mi><mo separator="true">,</mo><mi>j</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">\text{input}[i, j]</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mopen">[</span><span class="mord mathnormal">i</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span><span class="mclose">]</span></span></span></span></span> of the input tensor.
diff --git a/main/generated/torch.nn.functional.dropout2d.html b/main/generated/torch.nn.functional.dropout2d.html
index eee8ec9a52c..006e47e2c69 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.dropout2d<a class="headerlink" href="#torch-nn-functional-dropout2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.dropout2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels (a channel is a 2D feature map).</p>
 <p>For example, the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
 batched input is a 2D tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo stretchy="false">[</mo><mi>i</mi><mo separator="true">,</mo><mi>j</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">\text{input}[i, j]</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mopen">[</span><span class="mord mathnormal">i</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span><span class="mclose">]</span></span></span></span></span> of the input tensor.
diff --git a/main/generated/torch.nn.functional.dropout3d.html b/main/generated/torch.nn.functional.dropout3d.html
index 10268965b42..36cbc0fbd3e 100644
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@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.dropout3d<a class="headerlink" href="#torch-nn-functional-dropout3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.dropout3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels (a channel is a 3D feature map).</p>
 <p>For example, the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
 batched input is a 3D tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo stretchy="false">[</mo><mi>i</mi><mo separator="true">,</mo><mi>j</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">\text{input}[i, j]</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mopen">[</span><span class="mord mathnormal">i</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span><span class="mclose">]</span></span></span></span></span> of the input tensor.
diff --git a/main/generated/torch.nn.functional.elu.html b/main/generated/torch.nn.functional.elu.html
index 2b7271c80e8..f4d27472257 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.elu<a class="headerlink" href="#torch-nn-functional-elu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.elu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">elu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#elu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1796"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.elu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">elu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#elu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1796"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.elu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Exponential Linear Unit (ELU) function element-wise.</p>
 <p>See <a class="reference internal" href="torch.nn.ELU.html#torch.nn.ELU" title="torch.nn.ELU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ELU</span></code></a> for more details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.elu_.html b/main/generated/torch.nn.functional.elu_.html
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+++ b/main/generated/torch.nn.functional.elu_.html
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diff --git a/main/generated/torch.nn.functional.embedding.html b/main/generated/torch.nn.functional.embedding.html
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+++ b/main/generated/torch.nn.functional.embedding.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.embedding<a class="headerlink" href="#torch-nn-functional-embedding" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.embedding">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.embedding" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.embedding" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generate a simple lookup table that looks up embeddings in a fixed dictionary and size.</p>
 <p>This module is often used to retrieve word embeddings using indices.
 The input to the module is a list of indices, and the embedding matrix,
diff --git a/main/generated/torch.nn.functional.embedding_bag.html b/main/generated/torch.nn.functional.embedding_bag.html
index 28900941fd5..9be05825d65 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.embedding_bag<a class="headerlink" href="#torch-nn-functional-embedding-bag" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.embedding_bag">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">embedding_bag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_sample_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#embedding_bag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.embedding_bag" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">embedding_bag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_sample_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#embedding_bag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.embedding_bag" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute sums, means or maxes of <cite>bags</cite> of embeddings.</p>
 <p>Calculation is done without instantiating the intermediate embeddings.
 See <a class="reference internal" href="torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag" title="torch.nn.EmbeddingBag"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.EmbeddingBag</span></code></a> for more details.</p>
diff --git a/main/generated/torch.nn.functional.feature_alpha_dropout.html b/main/generated/torch.nn.functional.feature_alpha_dropout.html
index e6f7c77de30..c3aa9b20cd8 100644
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+++ b/main/generated/torch.nn.functional.feature_alpha_dropout.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.feature_alpha_dropout<a class="headerlink" href="#torch-nn-functional-feature-alpha-dropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.feature_alpha_dropout">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">feature_alpha_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#feature_alpha_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.feature_alpha_dropout" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">feature_alpha_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#feature_alpha_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.feature_alpha_dropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly masks out entire channels (a channel is a feature map).</p>
 <p>For example, the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the batch input
 is a tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo stretchy="false">[</mo><mi>i</mi><mo separator="true">,</mo><mi>j</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">\text{input}[i, j]</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mopen">[</span><span class="mord mathnormal">i</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span><span class="mclose">]</span></span></span></span></span> of the input tensor. Instead of
diff --git a/main/generated/torch.nn.functional.fold.html b/main/generated/torch.nn.functional.fold.html
index 8f210f2f946..77a5fead2dc 100644
--- a/main/generated/torch.nn.functional.fold.html
+++ b/main/generated/torch.nn.functional.fold.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.fold<a class="headerlink" href="#torch-nn-functional-fold" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.fold">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#fold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L5535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fold" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#fold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Combine an array of sliding local blocks into a large containing tensor.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.functional.fractional_max_pool2d.html b/main/generated/torch.nn.functional.fractional_max_pool2d.html
index 7501fbbb098..b1e6892421d 100644
--- a/main/generated/torch.nn.functional.fractional_max_pool2d.html
+++ b/main/generated/torch.nn.functional.fractional_max_pool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.fractional_max_pool2d<a class="headerlink" href="#torch-nn-functional-fractional-max-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.fractional_max_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fractional_max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fractional_max_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fractional_max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fractional_max_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies 2D fractional max pooling over an input signal composed of several input planes.</p>
 <p>Fractional MaxPooling is described in detail in the paper <a class="reference external" href="http://arxiv.org/abs/1412.6071">Fractional MaxPooling</a> by Ben Graham</p>
 <p>The max-pooling operation is applied in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by a stochastic
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.fractional_max_pool3d<a class="headerlink" href="#torch-nn-functional-fractional-max-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.fractional_max_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fractional_max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fractional_max_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fractional_max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fractional_max_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies 3D fractional max pooling over an input signal composed of several input planes.</p>
 <p>Fractional MaxPooling is described in detail in the paper <a class="reference external" href="http://arxiv.org/abs/1412.6071">Fractional MaxPooling</a> by Ben Graham</p>
 <p>The max-pooling operation is applied in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>T</mi><mo>×</mo><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kT \times kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by a stochastic
diff --git a/main/generated/torch.nn.functional.gaussian_nll_loss.html b/main/generated/torch.nn.functional.gaussian_nll_loss.html
index c0f2c8fa2c5..d8e4a7fbec9 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.gaussian_nll_loss<a class="headerlink" href="#torch-nn-functional-gaussian-nll-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.gaussian_nll_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">gaussian_nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#gaussian_nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.gaussian_nll_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">gaussian_nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#gaussian_nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.gaussian_nll_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gaussian negative log likelihood loss.</p>
 <p>See <a class="reference internal" href="torch.nn.GaussianNLLLoss.html#torch.nn.GaussianNLLLoss" title="torch.nn.GaussianNLLLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianNLLLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.gelu.html b/main/generated/torch.nn.functional.gelu.html
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diff --git a/main/generated/torch.nn.functional.glu.html b/main/generated/torch.nn.functional.glu.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.glu<a class="headerlink" href="#torch-nn-functional-glu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.glu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">glu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#glu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.glu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">glu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#glu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.glu" title="Permalink to this definition">¶</a></dt>
 <dd><p>The gated linear unit. Computes:</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>GLU</mtext><mo stretchy="false">(</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">)</mo><mo>=</mo><mi>a</mi><mo>⊗</mo><mi>σ</mi><mo stretchy="false">(</mo><mi>b</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{GLU}(a, b) = a \otimes \sigma(b)
diff --git a/main/generated/torch.nn.functional.grid_sample.html b/main/generated/torch.nn.functional.grid_sample.html
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.grid_sample<a class="headerlink" href="#torch-nn-functional-grid-sample" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.grid_sample">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">grid_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bilinear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#grid_sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L4867"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.grid_sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">grid_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bilinear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#grid_sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4867"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.grid_sample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute grid sample.</p>
 <p>Given an <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and a flow-field <code class="xref py py-attr docutils literal notranslate"><span class="pre">grid</span></code>, computes the
 <code class="docutils literal notranslate"><span class="pre">output</span></code> using <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> values and pixel locations from <code class="xref py py-attr docutils literal notranslate"><span class="pre">grid</span></code>.</p>
diff --git a/main/generated/torch.nn.functional.group_norm.html b/main/generated/torch.nn.functional.group_norm.html
index e502e632b9a..708f5d756db 100644
--- a/main/generated/torch.nn.functional.group_norm.html
+++ b/main/generated/torch.nn.functional.group_norm.html
@@ -300,7 +300,7 @@
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.group_norm<a class="headerlink" href="#torch-nn-functional-group-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.group_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">group_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#group_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.group_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">group_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#group_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.group_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Group Normalization for last certain number of dimensions.</p>
 <p>See <a class="reference internal" href="torch.nn.GroupNorm.html#torch.nn.GroupNorm" title="torch.nn.GroupNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupNorm</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.gumbel_softmax.html b/main/generated/torch.nn.functional.gumbel_softmax.html
index 4ee8f43bff8..a49422f2002 100644
--- a/main/generated/torch.nn.functional.gumbel_softmax.html
+++ b/main/generated/torch.nn.functional.gumbel_softmax.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.gumbel_softmax<a class="headerlink" href="#torch-nn-functional-gumbel-softmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.gumbel_softmax">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">gumbel_softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hard</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#gumbel_softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.gumbel_softmax" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">gumbel_softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hard</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#gumbel_softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.gumbel_softmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sample from the Gumbel-Softmax distribution (<a class="reference external" href="https://arxiv.org/abs/1611.00712">Link 1</a>  <a class="reference external" href="https://arxiv.org/abs/1611.01144">Link 2</a>) and optionally discretize.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.functional.hardshrink.html b/main/generated/torch.nn.functional.hardshrink.html
index aa2214e4cfa..2bc731e7326 100644
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@@ -300,7 +300,7 @@
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diff --git a/main/generated/torch.nn.functional.hardsigmoid.html b/main/generated/torch.nn.functional.hardsigmoid.html
index c7e53f640be..4d10fd91e6d 100644
--- a/main/generated/torch.nn.functional.hardsigmoid.html
+++ b/main/generated/torch.nn.functional.hardsigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.hardsigmoid<a class="headerlink" href="#torch-nn-functional-hardsigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.hardsigmoid">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardsigmoid" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardsigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Hardsigmoid function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>Hardsigmoid</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mrow><mo fence="true">{</mo><mtable rowspacing="0.36em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>0</mn></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext>if </mtext><mi>x</mi><mo>≤</mo><mo>−</mo><mn>3</mn><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>1</mn></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext>if </mtext><mi>x</mi><mo>≥</mo><mo>+</mo><mn>3</mn><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>x</mi><mi mathvariant="normal">/</mi><mn>6</mn><mo>+</mo><mn>1</mn><mi mathvariant="normal">/</mi><mn>2</mn></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mtext>otherwise</mtext></mstyle></mtd></mtr></mtable></mrow></mrow><annotation encoding="application/x-tex">\text{Hardsigmoid}(x) = \begin{cases}
diff --git a/main/generated/torch.nn.functional.hardswish.html b/main/generated/torch.nn.functional.hardswish.html
index aa306c933dc..6ed1eb47ab7 100644
--- a/main/generated/torch.nn.functional.hardswish.html
+++ b/main/generated/torch.nn.functional.hardswish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.hardswish<a class="headerlink" href="#torch-nn-functional-hardswish" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.hardswish">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardswish" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardswish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply hardswish function, element-wise.</p>
 <p>Follows implementation as described in the paper:
 <a class="reference external" href="https://arxiv.org/abs/1905.02244">Searching for MobileNetV3</a>.</p>
diff --git a/main/generated/torch.nn.functional.hardtanh.html b/main/generated/torch.nn.functional.hardtanh.html
index c04e63df132..1d758a0f90c 100644
--- a/main/generated/torch.nn.functional.hardtanh.html
+++ b/main/generated/torch.nn.functional.hardtanh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.hardtanh<a class="headerlink" href="#torch-nn-functional-hardtanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.hardtanh">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1745"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardtanh" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1745"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardtanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the HardTanh function element-wise. See <a class="reference internal" href="torch.nn.Hardtanh.html#torch.nn.Hardtanh" title="torch.nn.Hardtanh"><code class="xref py py-class docutils literal notranslate"><span class="pre">Hardtanh</span></code></a> for more
 details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.hardtanh_.html b/main/generated/torch.nn.functional.hardtanh_.html
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diff --git a/main/generated/torch.nn.functional.hinge_embedding_loss.html b/main/generated/torch.nn.functional.hinge_embedding_loss.html
index 6970ab1f714..54902b24484 100644
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.hinge_embedding_loss<a class="headerlink" href="#torch-nn-functional-hinge-embedding-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.hinge_embedding_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hinge_embedding_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#hinge_embedding_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hinge_embedding_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hinge_embedding_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#hinge_embedding_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hinge_embedding_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.HingeEmbeddingLoss.html#torch.nn.HingeEmbeddingLoss" title="torch.nn.HingeEmbeddingLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">HingeEmbeddingLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.huber_loss.html b/main/generated/torch.nn.functional.huber_loss.html
index db53f24c904..c0b8ffe6e61 100644
--- a/main/generated/torch.nn.functional.huber_loss.html
+++ b/main/generated/torch.nn.functional.huber_loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.huber_loss<a class="headerlink" href="#torch-nn-functional-huber-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.huber_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">huber_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#huber_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3696"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.huber_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">huber_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#huber_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3696"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.huber_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Huber loss, with optional weighting.</p>
 <p>Function uses a squared term if the absolute
 element-wise error falls below delta and a delta-scaled L1 term otherwise.</p>
diff --git a/main/generated/torch.nn.functional.instance_norm.html b/main/generated/torch.nn.functional.instance_norm.html
index bd3ddb6e32f..0409665c6de 100644
--- a/main/generated/torch.nn.functional.instance_norm.html
+++ b/main/generated/torch.nn.functional.instance_norm.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.instance_norm<a class="headerlink" href="#torch-nn-functional-instance-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.instance_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">instance_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_input_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#instance_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.instance_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">instance_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_input_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#instance_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.instance_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Instance Normalization independently for each channel in every data sample within a batch.</p>
 <p>See <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm1d</span></code></a>, <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm2d</span></code></a>,
 <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm3d</span></code></a> for details.</p>
diff --git a/main/generated/torch.nn.functional.interpolate.html b/main/generated/torch.nn.functional.interpolate.html
index 547f18413c7..d787611d337 100644
--- a/main/generated/torch.nn.functional.interpolate.html
+++ b/main/generated/torch.nn.functional.interpolate.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.interpolate<a class="headerlink" href="#torch-nn-functional-interpolate" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.interpolate">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompute_scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">antialias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#interpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L4453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.interpolate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompute_scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">antialias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#interpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.interpolate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Down/up samples the input.</p>
 <p>Tensor interpolated to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
diff --git a/main/generated/torch.nn.functional.kl_div.html b/main/generated/torch.nn.functional.kl_div.html
index ff4bf26f829..7b7a42ecca3 100644
--- a/main/generated/torch.nn.functional.kl_div.html
+++ b/main/generated/torch.nn.functional.kl_div.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.kl_div<a class="headerlink" href="#torch-nn-functional-kl-div" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.kl_div">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">kl_div</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#kl_div"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.kl_div" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">kl_div</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#kl_div"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.kl_div" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the KL Divergence loss.</p>
 <p>Refer - The <a class="reference external" href="https://en.wikipedia.org/wiki/Kullback-Leibler_divergence">Kullback-Leibler divergence Loss</a></p>
 <p>See <a class="reference internal" href="torch.nn.KLDivLoss.html#torch.nn.KLDivLoss" title="torch.nn.KLDivLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">KLDivLoss</span></code></a> for details.</p>
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index c1605f1c9fd..c85c1724f52 100644
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 <h1>torch.nn.functional.l1_loss<a class="headerlink" href="#torch-nn-functional-l1-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.l1_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">l1_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#l1_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3776"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.l1_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">l1_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#l1_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3776"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.l1_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function that takes the mean element-wise absolute value difference.</p>
 <p>See <a class="reference internal" href="torch.nn.L1Loss.html#torch.nn.L1Loss" title="torch.nn.L1Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">L1Loss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.layer_norm.html b/main/generated/torch.nn.functional.layer_norm.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.layer_norm<a class="headerlink" href="#torch-nn-functional-layer-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.layer_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">layer_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#layer_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.layer_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">layer_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#layer_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.layer_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Layer Normalization for last certain number of dimensions.</p>
 <p>See <a class="reference internal" href="torch.nn.LayerNorm.html#torch.nn.LayerNorm" title="torch.nn.LayerNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">LayerNorm</span></code></a> for details.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.leaky_relu<a class="headerlink" href="#torch-nn-functional-leaky-relu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.leaky_relu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">leaky_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#leaky_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.leaky_relu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">leaky_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#leaky_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.leaky_relu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise,
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>LeakyReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mtext>negative_slope</mtext><mo>∗</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">LeakyReLU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.0044em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">negative_slope</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.LeakyReLU.html#torch.nn.LeakyReLU" title="torch.nn.LeakyReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">LeakyReLU</span></code></a> for more details.</p>
diff --git a/main/generated/torch.nn.functional.leaky_relu_.html b/main/generated/torch.nn.functional.leaky_relu_.html
index 1a371551306..2bb18ba5d3a 100644
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diff --git a/main/generated/torch.nn.functional.linear.html b/main/generated/torch.nn.functional.linear.html
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diff --git a/main/generated/torch.nn.functional.local_response_norm.html b/main/generated/torch.nn.functional.local_response_norm.html
index 568bad2c679..c7122dc17de 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.local_response_norm<a class="headerlink" href="#torch-nn-functional-local-response-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.local_response_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">local_response_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#local_response_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2970"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.local_response_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">local_response_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#local_response_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2970"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.local_response_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply local response normalization over an input signal.</p>
 <p>The input signal is composed of several input planes, where channels occupy the second dimension.
 Normalization is applied across channels.</p>
diff --git a/main/generated/torch.nn.functional.log_softmax.html b/main/generated/torch.nn.functional.log_softmax.html
index 803e6e5ae5f..ad4af7adf97 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.log_softmax<a class="headerlink" href="#torch-nn-functional-log-softmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.log_softmax">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">log_softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#log_softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.log_softmax" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">log_softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#log_softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.log_softmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a softmax followed by a logarithm.</p>
 <p>While mathematically equivalent to log(softmax(x)), doing these two
 operations separately is slower and numerically unstable. This function
diff --git a/main/generated/torch.nn.functional.logsigmoid.html b/main/generated/torch.nn.functional.logsigmoid.html
index d069b126138..3cd8ce00415 100644
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+++ b/main/generated/torch.nn.functional.logsigmoid.html
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diff --git a/main/generated/torch.nn.functional.lp_pool1d.html b/main/generated/torch.nn.functional.lp_pool1d.html
index 20644d46c1a..f23041f6e4a 100644
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+++ b/main/generated/torch.nn.functional.lp_pool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.lp_pool1d<a class="headerlink" href="#torch-nn-functional-lp-pool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.lp_pool1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 1D power-average pooling over an input signal composed of several input planes.</p>
 <p>If the sum of all inputs to the power of <cite>p</cite> is
 zero, the gradient is set to zero as well.</p>
diff --git a/main/generated/torch.nn.functional.lp_pool2d.html b/main/generated/torch.nn.functional.lp_pool2d.html
index 1f92c75fc0f..610e3facd21 100644
--- a/main/generated/torch.nn.functional.lp_pool2d.html
+++ b/main/generated/torch.nn.functional.lp_pool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.lp_pool2d<a class="headerlink" href="#torch-nn-functional-lp-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.lp_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 2D power-average pooling over an input signal composed of several input planes.</p>
 <p>If the sum of all inputs to the power of <cite>p</cite> is
 zero, the gradient is set to zero as well.</p>
diff --git a/main/generated/torch.nn.functional.lp_pool3d.html b/main/generated/torch.nn.functional.lp_pool3d.html
index 4e2063e2ea7..7b962e3e4f3 100644
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+++ b/main/generated/torch.nn.functional.lp_pool3d.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.lp_pool3d<a class="headerlink" href="#torch-nn-functional-lp-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.lp_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1076"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1076"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 3D power-average pooling over an input signal composed of several input planes.</p>
 <p>If the sum of all inputs to the power of <cite>p</cite> is
 zero, the gradient is set to zero as well.</p>
diff --git a/main/generated/torch.nn.functional.margin_ranking_loss.html b/main/generated/torch.nn.functional.margin_ranking_loss.html
index 550f9e84d5e..3aa1e915234 100644
--- a/main/generated/torch.nn.functional.margin_ranking_loss.html
+++ b/main/generated/torch.nn.functional.margin_ranking_loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.margin_ranking_loss<a class="headerlink" href="#torch-nn-functional-margin-ranking-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.margin_ranking_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">margin_ranking_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#margin_ranking_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.margin_ranking_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">margin_ranking_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#margin_ranking_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.margin_ranking_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.MarginRankingLoss.html#torch.nn.MarginRankingLoss" title="torch.nn.MarginRankingLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MarginRankingLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.max_pool1d.html b/main/generated/torch.nn.functional.max_pool1d.html
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+++ b/main/generated/torch.nn.functional.max_pool1d.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_pool1d<a class="headerlink" href="#torch-nn-functional-max-pool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_pool1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D max pooling over an input signal composed of several input
 planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.nn.functional.max_pool2d.html b/main/generated/torch.nn.functional.max_pool2d.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_pool2d<a class="headerlink" href="#torch-nn-functional-max-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D max pooling over an input signal composed of several input
 planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.nn.functional.max_pool3d.html b/main/generated/torch.nn.functional.max_pool3d.html
index 0797e00f4b4..eac0ff20fdd 100644
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+++ b/main/generated/torch.nn.functional.max_pool3d.html
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_pool3d<a class="headerlink" href="#torch-nn-functional-max-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D max pooling over an input signal composed of several input
 planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.nn.functional.max_unpool1d.html b/main/generated/torch.nn.functional.max_unpool1d.html
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+++ b/main/generated/torch.nn.functional.max_unpool1d.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_unpool1d<a class="headerlink" href="#torch-nn-functional-max-unpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_unpool1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code>.</p>
 <p>See <a class="reference internal" href="torch.nn.MaxUnpool1d.html#torch.nn.MaxUnpool1d" title="torch.nn.MaxUnpool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool1d</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.max_unpool2d.html b/main/generated/torch.nn.functional.max_unpool2d.html
index cf43a9c96dc..cfd7eed8c7a 100644
--- a/main/generated/torch.nn.functional.max_unpool2d.html
+++ b/main/generated/torch.nn.functional.max_unpool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_unpool2d<a class="headerlink" href="#torch-nn-functional-max-unpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_unpool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1010"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1010"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code>.</p>
 <p>See <a class="reference internal" href="torch.nn.MaxUnpool2d.html#torch.nn.MaxUnpool2d" title="torch.nn.MaxUnpool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool2d</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.max_unpool3d.html b/main/generated/torch.nn.functional.max_unpool3d.html
index b9988d729c7..6c4cd953c7c 100644
--- a/main/generated/torch.nn.functional.max_unpool3d.html
+++ b/main/generated/torch.nn.functional.max_unpool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_unpool3d<a class="headerlink" href="#torch-nn-functional-max-unpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_unpool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code>.</p>
 <p>See <a class="reference internal" href="torch.nn.MaxUnpool3d.html#torch.nn.MaxUnpool3d" title="torch.nn.MaxUnpool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool3d</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.mish.html b/main/generated/torch.nn.functional.mish.html
index 5514a033e12..72d193b873b 100644
--- a/main/generated/torch.nn.functional.mish.html
+++ b/main/generated/torch.nn.functional.mish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.mish<a class="headerlink" href="#torch-nn-functional-mish" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.mish">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">mish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#mish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.mish" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">mish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#mish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.mish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Mish function, element-wise.</p>
 <p>Mish: A Self Regularized Non-Monotonic Neural Activation Function.</p>
 <div class="math">
diff --git a/main/generated/torch.nn.functional.mse_loss.html b/main/generated/torch.nn.functional.mse_loss.html
index 8a8d1c6a40c..fbcf75fef16 100644
--- a/main/generated/torch.nn.functional.mse_loss.html
+++ b/main/generated/torch.nn.functional.mse_loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.mse_loss<a class="headerlink" href="#torch-nn-functional-mse-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.mse_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">mse_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#mse_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.mse_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">mse_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#mse_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.mse_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Measures the element-wise mean squared error, with optional weighting.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.functional.multi_margin_loss.html b/main/generated/torch.nn.functional.multi_margin_loss.html
index ada552e447d..754d09698b3 100644
--- a/main/generated/torch.nn.functional.multi_margin_loss.html
+++ b/main/generated/torch.nn.functional.multi_margin_loss.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.multi_margin_loss<a class="headerlink" href="#torch-nn-functional-multi-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.multi_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multi_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multi_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L4113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multi_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multi_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multi_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multi_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.MultiMarginLoss.html#torch.nn.MultiMarginLoss" title="torch.nn.MultiMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.multilabel_margin_loss.html b/main/generated/torch.nn.functional.multilabel_margin_loss.html
index c85ab97f228..264d1678008 100644
--- a/main/generated/torch.nn.functional.multilabel_margin_loss.html
+++ b/main/generated/torch.nn.functional.multilabel_margin_loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.multilabel_margin_loss<a class="headerlink" href="#torch-nn-functional-multilabel-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.multilabel_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multilabel_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multilabel_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3977"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multilabel_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multilabel_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multilabel_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3977"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multilabel_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.MultiLabelMarginLoss.html#torch.nn.MultiLabelMarginLoss" title="torch.nn.MultiLabelMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiLabelMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.multilabel_soft_margin_loss.html b/main/generated/torch.nn.functional.multilabel_soft_margin_loss.html
index f609d86cb52..1fc430c0df8 100644
--- a/main/generated/torch.nn.functional.multilabel_soft_margin_loss.html
+++ b/main/generated/torch.nn.functional.multilabel_soft_margin_loss.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.multilabel_soft_margin_loss<a class="headerlink" href="#torch-nn-functional-multilabel-soft-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.multilabel_soft_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multilabel_soft_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multilabel_soft_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L4034"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multilabel_soft_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multilabel_soft_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multilabel_soft_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4034"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multilabel_soft_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.MultiLabelSoftMarginLoss.html#torch.nn.MultiLabelSoftMarginLoss" title="torch.nn.MultiLabelSoftMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiLabelSoftMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.nll_loss.html b/main/generated/torch.nn.functional.nll_loss.html
index 049ddfb442c..bf337cac1c2 100644
--- a/main/generated/torch.nn.functional.nll_loss.html
+++ b/main/generated/torch.nn.functional.nll_loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.nll_loss<a class="headerlink" href="#torch-nn-functional-nll-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.nll_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.nll_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.nll_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the negative log likelihood loss.</p>
 <p>See <a class="reference internal" href="torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">NLLLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.normalize.html b/main/generated/torch.nn.functional.normalize.html
index 33ad2dcb0ec..97a24065a45 100644
--- a/main/generated/torch.nn.functional.normalize.html
+++ b/main/generated/torch.nn.functional.normalize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.normalize<a class="headerlink" href="#torch-nn-functional-normalize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.normalize">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#normalize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L5456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.normalize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#normalize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.normalize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>L</mi><mi>p</mi></msub></mrow><annotation encoding="application/x-tex">L_p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.9694em;vertical-align:-0.2861em;"></span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span></span></span></span></span> normalization of inputs over specified dimension.</p>
 <p>For a tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> of sizes <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><msub><mi>n</mi><mn>0</mn></msub><mo separator="true">,</mo><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mo separator="true">,</mo><msub><mi>n</mi><mrow><mi>d</mi><mi>i</mi><mi>m</mi></mrow></msub><mo separator="true">,</mo><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mo separator="true">,</mo><msub><mi>n</mi><mi>k</mi></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(n_0, ..., n_{dim}, ..., n_k)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">n</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">0</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">...</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">n</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">d</span><span class="mord mathnormal mtight">im</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">...</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">n</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>, each
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>n</mi><mrow><mi>d</mi><mi>i</mi><mi>m</mi></mrow></msub></mrow><annotation encoding="application/x-tex">n_{dim}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">n</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">d</span><span class="mord mathnormal mtight">im</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> -element vector <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>v</mi></mrow><annotation encoding="application/x-tex">v</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">v</span></span></span></span></span> along dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is transformed as</p>
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diff --git a/main/generated/torch.nn.functional.pad.html b/main/generated/torch.nn.functional.pad.html
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 <h1>torch.nn.functional.pad<a class="headerlink" href="#torch-nn-functional-pad" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.pad">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#pad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L5128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.pad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#pad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.pad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads tensor.</p>
 <dl class="simple">
 <dt>Padding size:</dt><dd><p>The padding size by which to pad some dimensions of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>
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 <h1>torch.nn.functional.poisson_nll_loss<a class="headerlink" href="#torch-nn-functional-poisson-nll-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.poisson_nll_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">poisson_nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#poisson_nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.poisson_nll_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">poisson_nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#poisson_nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.poisson_nll_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Poisson negative log likelihood loss.</p>
 <p>See <a class="reference internal" href="torch.nn.PoissonNLLLoss.html#torch.nn.PoissonNLLLoss" title="torch.nn.PoissonNLLLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">PoissonNLLLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.relu<a class="headerlink" href="#torch-nn-functional-relu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.relu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1693"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.relu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1693"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.relu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the rectified linear unit function element-wise. See
 <a class="reference internal" href="torch.nn.ReLU.html#torch.nn.ReLU" title="torch.nn.ReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ReLU</span></code></a> for more details.</p>
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 <h1>torch.nn.functional.relu6<a class="headerlink" href="#torch-nn-functional-relu6" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.relu6">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">relu6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#relu6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.relu6" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">relu6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#relu6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.relu6" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU6</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo separator="true">,</mo><mn>6</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU6}(x) = \min(\max(0,x), 6)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">ReLU6</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">6</span><span class="mclose">)</span></span></span></span></span>.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.rms_norm<a class="headerlink" href="#torch-nn-functional-rms-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.rms_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">rms_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#rms_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.rms_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">rms_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#rms_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.rms_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Root Mean Square Layer Normalization.</p>
 <p>See <a class="reference internal" href="torch.nn.RMSNorm.html#torch.nn.RMSNorm" title="torch.nn.RMSNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">RMSNorm</span></code></a> for details.</p>
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+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">rrelu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span> <span class="pre">/</span> <span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span> <span class="pre">/</span> <span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#rrelu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.rrelu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomized leaky ReLU.</p>
 <p>See <a class="reference internal" href="torch.nn.RReLU.html#torch.nn.RReLU" title="torch.nn.RReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">RReLU</span></code></a> for more details.</p>
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diff --git a/main/generated/torch.nn.functional.selu.html b/main/generated/torch.nn.functional.selu.html
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-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">selu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#selu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.selu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">selu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#selu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.selu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise,
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>SELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>s</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>e</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">SELU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">sc</span><span class="mord mathnormal">a</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mord mathnormal">e</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">1</span><span class="mclose">)))</span></span></span></span></span>,
 with <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi><mo>=</mo><mn>1.6732632423543772848170429916717</mn></mrow><annotation encoding="application/x-tex">\alpha=1.6732632423543772848170429916717</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1.6732632423543772848170429916717</span></span></span></span></span> and
diff --git a/main/generated/torch.nn.functional.sigmoid.html b/main/generated/torch.nn.functional.sigmoid.html
index 31f0c22de6d..56914047879 100644
--- a/main/generated/torch.nn.functional.sigmoid.html
+++ b/main/generated/torch.nn.functional.sigmoid.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.sigmoid<a class="headerlink" href="#torch-nn-functional-sigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.sigmoid">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.sigmoid" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.sigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Sigmoid</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Sigmoid</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.3651em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.Sigmoid.html#torch.nn.Sigmoid" title="torch.nn.Sigmoid"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sigmoid</span></code></a> for more details.</p>
 </dd></dl>
diff --git a/main/generated/torch.nn.functional.silu.html b/main/generated/torch.nn.functional.silu.html
index c4191688bf0..fe835935690 100644
--- a/main/generated/torch.nn.functional.silu.html
+++ b/main/generated/torch.nn.functional.silu.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.silu<a class="headerlink" href="#torch-nn-functional-silu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.silu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">silu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#silu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.silu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">silu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#silu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.silu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Sigmoid Linear Unit (SiLU) function, element-wise.</p>
 <p>The SiLU function is also known as the swish function.</p>
 <div class="math">
diff --git a/main/generated/torch.nn.functional.smooth_l1_loss.html b/main/generated/torch.nn.functional.smooth_l1_loss.html
index c7edb5f7b70..bdee734713e 100644
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+++ b/main/generated/torch.nn.functional.smooth_l1_loss.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.smooth_l1_loss<a class="headerlink" href="#torch-nn-functional-smooth-l1-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.smooth_l1_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">smooth_l1_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#smooth_l1_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L3648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.smooth_l1_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">smooth_l1_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#smooth_l1_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.smooth_l1_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the Smooth L1 loss.</p>
 <p>Function uses a squared term if the absolute
 element-wise error falls below beta and an L1 term otherwise.</p>
diff --git a/main/generated/torch.nn.functional.soft_margin_loss.html b/main/generated/torch.nn.functional.soft_margin_loss.html
index 6f31bc6a2dc..99372ae8e2e 100644
--- a/main/generated/torch.nn.functional.soft_margin_loss.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.soft_margin_loss<a class="headerlink" href="#torch-nn-functional-soft-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.soft_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">soft_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#soft_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L4005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.soft_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">soft_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#soft_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.soft_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.SoftMarginLoss.html#torch.nn.SoftMarginLoss" title="torch.nn.SoftMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">SoftMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
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index a8dc4c8a759..572746bfbbd 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.softmax<a class="headerlink" href="#torch-nn-functional-softmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.softmax">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softmax" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a softmax function.</p>
 <p>Softmax is defined as:</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Softmax</mtext><mo stretchy="false">(</mo><msub><mi>x</mi><mi>i</mi></msub><mo stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>x</mi><mi>i</mi></msub><mo stretchy="false">)</mo></mrow><mrow><msub><mo>∑</mo><mi>j</mi></msub><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>x</mi><mi>j</mi></msub><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Softmax</span></span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">i</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.6772em;vertical-align:-0.6672em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.01em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mop op-symbol small-op mtight" style="position:relative;top:0em;">∑</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1496em;"><span style="top:-2.1786em;margin-left:0em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mathnormal mtight" style="margin-right:0.05724em;">j</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.4603em;"><span></span></span></span></span></span></span><span class="mspace mtight" style="margin-right:0.1952em;"></span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight"><span class="mord mathnormal mtight">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3281em;"><span style="top:-2.357em;margin-left:0em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mathnormal mtight" style="margin-right:0.05724em;">j</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2819em;"><span></span></span></span></span></span></span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.485em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight"><span class="mord mathnormal mtight">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3281em;"><span style="top:-2.357em;margin-left:0em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.143em;"><span></span></span></span></span></span></span><span class="mclose mtight">)</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.6672em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.softmin<a class="headerlink" href="#torch-nn-functional-softmin" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.softmin">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#softmin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softmin" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#softmin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softmin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a softmin function.</p>
 <p>Note that <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Softmin</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mtext>Softmax</mtext><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Softmin}(x) = \text{Softmax}(-x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Softmin</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Softmax</span></span><span class="mopen">(</span><span class="mord">−</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span>. See softmax definition for mathematical formula.</p>
 <p>See <a class="reference internal" href="torch.nn.Softmin.html#torch.nn.Softmin" title="torch.nn.Softmin"><code class="xref py py-class docutils literal notranslate"><span class="pre">Softmin</span></code></a> for more details.</p>
diff --git a/main/generated/torch.nn.functional.softplus.html b/main/generated/torch.nn.functional.softplus.html
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diff --git a/main/generated/torch.nn.functional.softsign.html b/main/generated/torch.nn.functional.softsign.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.softsign<a class="headerlink" href="#torch-nn-functional-softsign" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.softsign">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softsign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#softsign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2030"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softsign" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softsign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#softsign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2030"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softsign" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise, the function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>SoftSign</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mi>x</mi><mrow><mn>1</mn><mo>+</mo><mi mathvariant="normal">∣</mi><mi>x</mi><mi mathvariant="normal">∣</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{SoftSign}(x) = \frac{x}{1 + |x|}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">SoftSign</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.2154em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.6954em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mord mtight">∣</span><span class="mord mathnormal mtight">x</span><span class="mord mtight">∣</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">x</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.Softsign.html#torch.nn.Softsign" title="torch.nn.Softsign"><code class="xref py py-class docutils literal notranslate"><span class="pre">Softsign</span></code></a> for more details.</p>
 </dd></dl>
diff --git a/main/generated/torch.nn.functional.tanh.html b/main/generated/torch.nn.functional.tanh.html
index 8c5291298db..e36031912a0 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.tanh<a class="headerlink" href="#torch-nn-functional-tanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.tanh">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">tanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#tanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.tanh" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">tanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#tanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.tanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise,
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Tanh</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>−</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><mrow><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Tanh</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">tanh</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.53em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.01em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span><span class="mbin mtight">+</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.485em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span><span class="mbin mtight">−</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.Tanh.html#torch.nn.Tanh" title="torch.nn.Tanh"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tanh</span></code></a> for more details.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.tanhshrink<a class="headerlink" href="#torch-nn-functional-tanhshrink" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.tanhshrink">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">tanhshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#tanhshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L2018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.tanhshrink" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">tanhshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#tanhshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.tanhshrink" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Tanhshrink</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mo>−</mo><mtext>Tanh</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Tanhshrink}(x) = x - \text{Tanh}(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Tanhshrink</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Tanh</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.Tanhshrink.html#torch.nn.Tanhshrink" title="torch.nn.Tanhshrink"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tanhshrink</span></code></a> for more details.</p>
 </dd></dl>
diff --git a/main/generated/torch.nn.functional.threshold.html b/main/generated/torch.nn.functional.threshold.html
index 9017b9c6ebd..f0bb922528e 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.threshold<a class="headerlink" href="#torch-nn-functional-threshold" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.threshold">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L1657"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.threshold" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1657"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.threshold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a threshold to each element of the input Tensor.</p>
 <p>See <a class="reference internal" href="torch.nn.Threshold.html#torch.nn.Threshold" title="torch.nn.Threshold"><code class="xref py py-class docutils literal notranslate"><span class="pre">Threshold</span></code></a> for more details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.threshold_.html b/main/generated/torch.nn.functional.threshold_.html
index 5cd32726d82..69a1c92aead 100644
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diff --git a/main/generated/torch.nn.functional.torch.nn.parallel.data_parallel.html b/main/generated/torch.nn.functional.torch.nn.parallel.data_parallel.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.torch.nn.parallel.data_parallel<a class="headerlink" href="#torch-nn-functional-torch-nn-parallel-data-parallel" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.parallel.data_parallel">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.parallel.</span></span><span class="sig-name descname"><span class="pre">data_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/data_parallel.html#data_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/data_parallel.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.data_parallel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.parallel.</span></span><span class="sig-name descname"><span class="pre">data_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/data_parallel.html#data_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/data_parallel.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.data_parallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Evaluate module(input) in parallel across the GPUs given in device_ids.</p>
 <p>This is the functional version of the DataParallel module.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.triplet_margin_loss.html b/main/generated/torch.nn.functional.triplet_margin_loss.html
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 <h1>torch.nn.functional.triplet_margin_loss<a class="headerlink" href="#torch-nn-functional-triplet-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.triplet_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">triplet_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">anchor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#triplet_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L5337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.triplet_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">triplet_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">anchor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#triplet_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.triplet_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the triplet loss between given input tensors and a margin greater than 0.</p>
 <p>See <a class="reference internal" href="torch.nn.TripletMarginLoss.html#torch.nn.TripletMarginLoss" title="torch.nn.TripletMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">TripletMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.triplet_margin_with_distance_loss.html b/main/generated/torch.nn.functional.triplet_margin_with_distance_loss.html
index ad44f395b2b..e98ab33a959 100644
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+++ b/main/generated/torch.nn.functional.triplet_margin_with_distance_loss.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
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 <h1>torch.nn.functional.triplet_margin_with_distance_loss<a class="headerlink" href="#torch-nn-functional-triplet-margin-with-distance-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.triplet_margin_with_distance_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">triplet_margin_with_distance_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">anchor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#triplet_margin_with_distance_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L5379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.triplet_margin_with_distance_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">triplet_margin_with_distance_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">anchor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#triplet_margin_with_distance_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.triplet_margin_with_distance_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the triplet margin loss for input tensors using a custom distance function.</p>
 <p>See <a class="reference internal" href="torch.nn.TripletMarginWithDistanceLoss.html#torch.nn.TripletMarginWithDistanceLoss" title="torch.nn.TripletMarginWithDistanceLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">TripletMarginWithDistanceLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.unfold.html b/main/generated/torch.nn.functional.unfold.html
index b21252b85d4..fc4a3fa88a1 100644
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     <div class="version">
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     </div>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.unfold<a class="headerlink" href="#torch-nn-functional-unfold" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.unfold">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">unfold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#unfold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L5497"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.unfold" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">unfold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#unfold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5497"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.unfold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extract sliding local blocks from a batched input tensor.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.functional.upsample.html b/main/generated/torch.nn.functional.upsample.html
index cf3d266b5b6..e1ff78137ae 100644
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.upsample<a class="headerlink" href="#torch-nn-functional-upsample" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.upsample">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L4312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsample input.</p>
 <p>Provided tensor is upsampled to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
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           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.upsample_bilinear<a class="headerlink" href="#torch-nn-functional-upsample-bilinear" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.upsample_bilinear">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample_bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L4821"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample_bilinear" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample_bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4821"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample_bilinear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input, using bilinear upsampling.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.functional.upsample_nearest.html b/main/generated/torch.nn.functional.upsample_nearest.html
index b8863c2a898..1c31c63f259 100644
--- a/main/generated/torch.nn.functional.upsample_nearest.html
+++ b/main/generated/torch.nn.functional.upsample_nearest.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.upsample_nearest<a class="headerlink" href="#torch-nn-functional-upsample-nearest" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.upsample_nearest">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/functional.py#L4753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample_nearest" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample_nearest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input, using nearest neighbours’ pixel values.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.lazy.LazyModuleMixin.html b/main/generated/torch.nn.modules.lazy.LazyModuleMixin.html
index ea387fbce12..6e352624352 100644
--- a/main/generated/torch.nn.modules.lazy.LazyModuleMixin.html
+++ b/main/generated/torch.nn.modules.lazy.LazyModuleMixin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyModuleMixin<a class="headerlink" href="#lazymodulemixin" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.modules.lazy.LazyModuleMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.modules.lazy.</span></span><span class="sig-name descname"><span class="pre">LazyModuleMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/lazy.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.modules.lazy.</span></span><span class="sig-name descname"><span class="pre">LazyModuleMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/lazy.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin" title="Permalink to this definition">¶</a></dt>
 <dd><p>A mixin for modules that lazily initialize parameters, also known as “lazy modules”.</p>
 <p>Modules that lazily initialize parameters, or “lazy modules”,
 derive the shapes of their parameters from the first input(s)
@@ -720,7 +720,7 @@ <h1>LazyModuleMixin<a class="headerlink" href="#lazymodulemixin" title="Permalin
 when the state is loaded. This prevents using initialized modules in different contexts.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.lazy.LazyModuleMixin.has_uninitialized_params">
-<span class="sig-name descname"><span class="pre">has_uninitialized_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin.has_uninitialized_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/lazy.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin.has_uninitialized_params" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">has_uninitialized_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin.has_uninitialized_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/lazy.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin.has_uninitialized_params" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if a module has parameters that are not initialized.</p>
 <dl class="field-list simple">
 </dl>
@@ -728,7 +728,7 @@ <h1>LazyModuleMixin<a class="headerlink" href="#lazymodulemixin" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.lazy.LazyModuleMixin.initialize_parameters">
-<span class="sig-name descname"><span class="pre">initialize_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin.initialize_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/lazy.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin.initialize_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">initialize_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin.initialize_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/lazy.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin.initialize_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize parameters according to the input batch properties.</p>
 <p>This adds an interface to isolate parameter initialization from the
 forward pass when doing parameter shape inference.</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_backward_hook.html b/main/generated/torch.nn.modules.module.register_module_backward_hook.html
index 90189f44979..5b317ac8b9c 100644
--- a/main/generated/torch.nn.modules.module.register_module_backward_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_backward_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_backward_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-backward-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_backward_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook common to all the modules.</p>
 <p>This function is deprecated in favor of
 <a class="reference internal" href="torch.nn.modules.module.register_module_full_backward_hook.html#torch.nn.modules.module.register_module_full_backward_hook" title="torch.nn.modules.module.register_module_full_backward_hook"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.modules.module.register_module_full_backward_hook()</span></code></a>
diff --git a/main/generated/torch.nn.modules.module.register_module_buffer_registration_hook.html b/main/generated/torch.nn.modules.module.register_module_buffer_registration_hook.html
index 38521097d47..dbfe52a8e0c 100644
--- a/main/generated/torch.nn.modules.module.register_module_buffer_registration_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_buffer_registration_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_buffer_registration_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-buffer-registration-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_buffer_registration_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_buffer_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_buffer_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_buffer_registration_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_buffer_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_buffer_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_buffer_registration_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a buffer registration hook common to all modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_forward_hook.html b/main/generated/torch.nn.modules.module.register_module_forward_hook.html
index 531db39502a..bdd3fdf91f1 100644
--- a/main/generated/torch.nn.modules.module.register_module_forward_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_forward_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_forward_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-forward-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_forward_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_forward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_forward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_forward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_forward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a global forward hook for all the modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_forward_pre_hook.html b/main/generated/torch.nn.modules.module.register_module_forward_pre_hook.html
index f8e5df36719..ac1b871826f 100644
--- a/main/generated/torch.nn.modules.module.register_module_forward_pre_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_forward_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_forward_pre_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-forward-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_forward_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_forward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_forward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward pre-hook common to all modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_full_backward_hook.html b/main/generated/torch.nn.modules.module.register_module_full_backward_hook.html
index 9a7564756be..ce8c1235342 100644
--- a/main/generated/torch.nn.modules.module.register_module_full_backward_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_full_backward_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_full_backward_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-full-backward-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_full_backward_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_full_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_full_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_full_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_full_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook common to all the modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_full_backward_pre_hook.html b/main/generated/torch.nn.modules.module.register_module_full_backward_pre_hook.html
index 9dd3f04dfdf..d387fd8fafd 100644
--- a/main/generated/torch.nn.modules.module.register_module_full_backward_pre_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_full_backward_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_full_backward_pre_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-full-backward-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_full_backward_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_full_backward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_full_backward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook common to all the modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_module_registration_hook.html b/main/generated/torch.nn.modules.module.register_module_module_registration_hook.html
index 8d68a806c5e..1b9d9ceeb20 100644
--- a/main/generated/torch.nn.modules.module.register_module_module_registration_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_module_registration_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_module_registration_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-module-registration-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_module_registration_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_module_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_module_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_module_registration_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_module_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_module_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_module_registration_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a module registration hook common to all modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_parameter_registration_hook.html b/main/generated/torch.nn.modules.module.register_module_parameter_registration_hook.html
index 657647c3404..80f25b2c6a3 100644
--- a/main/generated/torch.nn.modules.module.register_module_parameter_registration_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_parameter_registration_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_parameter_registration_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-parameter-registration-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_parameter_registration_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_parameter_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_parameter_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_parameter_registration_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_parameter_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_parameter_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_parameter_registration_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a parameter registration hook common to all modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.normalization.RMSNorm.html b/main/generated/torch.nn.modules.normalization.RMSNorm.html
index 96b72f3c762..adc274ef4b2 100644
--- a/main/generated/torch.nn.modules.normalization.RMSNorm.html
+++ b/main/generated/torch.nn.modules.normalization.RMSNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.modules.normalization.RMSNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.modules.normalization.</span></span><span class="sig-name descname"><span class="pre">RMSNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.modules.normalization.</span></span><span class="sig-name descname"><span class="pre">RMSNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Root Mean Square Layer Normalization over a mini-batch of inputs.</p>
 <p>This layer implements the operation as described in
 the paper <a class="reference external" href="https://arxiv.org/pdf/1910.07467.pdf">Root Mean Square Layer Normalization</a></p>
@@ -661,7 +661,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.normalization.RMSNorm.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extra information about the module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -672,7 +672,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.normalization.RMSNorm.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs forward pass.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -683,7 +683,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.normalization.RMSNorm.reset_parameters">
-<span class="sig-name descname"><span class="pre">reset_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.reset_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/normalization.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.reset_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.reset_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.reset_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Resets parameters based on their initialization used in __init__.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.nn.parallel.DistributedDataParallel.html b/main/generated/torch.nn.parallel.DistributedDataParallel.html
index 528ece136a8..4e2e7a2a5af 100644
--- a/main/generated/torch.nn.parallel.DistributedDataParallel.html
+++ b/main/generated/torch.nn.parallel.DistributedDataParallel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parallel.</span></span><span class="sig-name descname"><span class="pre">DistributedDataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">broadcast_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_sync</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket_cap_mb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">find_unused_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient_as_bucket_view</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">static_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay_all_reduce_named_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_to_hook_all_reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/distributed.py#L326"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parallel.</span></span><span class="sig-name descname"><span class="pre">DistributedDataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">broadcast_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_sync</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket_cap_mb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">find_unused_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient_as_bucket_view</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">static_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay_all_reduce_named_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_to_hook_all_reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L326"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement distributed data parallelism based on <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> at module level.</p>
 <p>This container provides data parallelism by synchronizing gradients
 across each model replica. The devices to synchronize across are
@@ -936,7 +936,7 @@ <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel.join">
-<span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">divide_by_initial_world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">throw_on_early_termination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/distributed.py#L1753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.join" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">divide_by_initial_world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">throw_on_early_termination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L1753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.join" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager for training with uneven inputs across processes in DDP.</p>
 <p>This context manager will keep track of already-joined DDP processes,
 and “shadow” the forward and backward passes by inserting collective
@@ -1032,7 +1032,7 @@ <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel.join_hook">
-<span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/distributed.py#L1859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.join_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L1859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.join_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>DDP join hook enables training on uneven inputs by mirroring communications in forward and backward passes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1061,7 +1061,7 @@ <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel.no_sync">
-<span class="sig-name descname"><span class="pre">no_sync</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.no_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/distributed.py#L1419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.no_sync" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">no_sync</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.no_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L1419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.no_sync" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager to disable gradient synchronizations across DDP processes.</p>
 <p>Within this context, gradients will be accumulated on module
 variables, which will later be synchronized in the first
@@ -1083,7 +1083,7 @@ <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel.register_comm_hook">
-<span class="sig-name descname"><span class="pre">register_comm_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.register_comm_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parallel/distributed.py#L1941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.register_comm_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_comm_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.register_comm_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L1941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.register_comm_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register communication hook for user-defined DDP aggregation of gradients across multiple workers.</p>
 <p>This hook would be very useful for researchers to try out new ideas. For
 example, this hook can be used to implement several algorithms like GossipGrad
diff --git a/main/generated/torch.nn.parameter.Buffer.html b/main/generated/torch.nn.parameter.Buffer.html
index a033de37098..7986adcc355 100644
--- a/main/generated/torch.nn.parameter.Buffer.html
+++ b/main/generated/torch.nn.parameter.Buffer.html
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.nn.parameter.Buffer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">Buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#Buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parameter.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.Buffer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">Buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#Buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.Buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>A kind of Tensor that should not be considered a model
 parameter. For example, BatchNorm’s <code class="docutils literal notranslate"><span class="pre">running_mean</span></code> is not a parameter, but is part of the module’s state.</p>
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diff --git a/main/generated/torch.nn.parameter.Parameter.html b/main/generated/torch.nn.parameter.Parameter.html
index 8689cb36fea..d46c507b350 100644
--- a/main/generated/torch.nn.parameter.Parameter.html
+++ b/main/generated/torch.nn.parameter.Parameter.html
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@@ -612,7 +612,7 @@
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 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.parameter.Parameter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">Parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#Parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parameter.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.Parameter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">Parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#Parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.Parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>A kind of Tensor that is to be considered a module parameter.</p>
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 very special property when used with <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> s - when they’re
diff --git a/main/generated/torch.nn.parameter.UninitializedBuffer.html b/main/generated/torch.nn.parameter.UninitializedBuffer.html
index 7e3f4c8d199..bf4fc670393 100644
--- a/main/generated/torch.nn.parameter.UninitializedBuffer.html
+++ b/main/generated/torch.nn.parameter.UninitializedBuffer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UninitializedBuffer<a class="headerlink" href="#uninitializedbuffer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.parameter.UninitializedBuffer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">UninitializedBuffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#UninitializedBuffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parameter.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedBuffer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">UninitializedBuffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#UninitializedBuffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedBuffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>A buffer that is not initialized.</p>
 <p>Uninitialized Buffer is a a special case of <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>
 where the shape of the data is still unknown.</p>
diff --git a/main/generated/torch.nn.parameter.UninitializedParameter.html b/main/generated/torch.nn.parameter.UninitializedParameter.html
index ed6226b3444..d4ddcbfa9ac 100644
--- a/main/generated/torch.nn.parameter.UninitializedParameter.html
+++ b/main/generated/torch.nn.parameter.UninitializedParameter.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UninitializedParameter<a class="headerlink" href="#uninitializedparameter" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.parameter.UninitializedParameter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">UninitializedParameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#UninitializedParameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parameter.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedParameter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">UninitializedParameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#UninitializedParameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedParameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>A parameter that is not initialized.</p>
 <p>Uninitialized Parameters are a special case of <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Parameter</span></code>
 where the shape of the data is still unknown.</p>
@@ -627,7 +627,7 @@ <h1>UninitializedParameter<a class="headerlink" href="#uninitializedparameter" t
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.parameter.UninitializedParameter.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/parameter.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedParameter.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedParameter.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter" title="torch.nn.parameter.Parameter"><code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.utils.clip_grad_norm.html b/main/generated/torch.nn.utils.clip_grad_norm.html
index d5959be2de9..52e50b45c63 100644
--- a/main/generated/torch.nn.utils.clip_grad_norm.html
+++ b/main/generated/torch.nn.utils.clip_grad_norm.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.clip_grad_norm<a class="headerlink" href="#torch-nn-utils-clip-grad-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.clip_grad_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/clip_grad.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clip the gradient norm of an iterable of parameters.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.utils.clip_grad_norm_.html b/main/generated/torch.nn.utils.clip_grad_norm_.html
index 0d3d15c8bfe..b00e199c749 100644
--- a/main/generated/torch.nn.utils.clip_grad_norm_.html
+++ b/main/generated/torch.nn.utils.clip_grad_norm_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.clip_grad_norm_<a class="headerlink" href="#torch-nn-utils-clip-grad-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.clip_grad_norm_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/clip_grad.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clip the gradient norm of an iterable of parameters.</p>
 <p>The norm is computed over the norms of the individual gradients of all parameters,
 as if the norms of the individual gradients were concatenated into a single vector.
diff --git a/main/generated/torch.nn.utils.clip_grad_value_.html b/main/generated/torch.nn.utils.clip_grad_value_.html
index c95fca06128..e68f9e5c034 100644
--- a/main/generated/torch.nn.utils.clip_grad_value_.html
+++ b/main/generated/torch.nn.utils.clip_grad_value_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.clip_grad_value_<a class="headerlink" href="#torch-nn-utils-clip-grad-value" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.clip_grad_value_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_value_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_value_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/clip_grad.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_value_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_value_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_value_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_value_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clip the gradients of an iterable of parameters at specified value.</p>
 <p>Gradients are modified in-place.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.utils.clip_grads_with_norm_.html b/main/generated/torch.nn.utils.clip_grads_with_norm_.html
index a80574301be..5a8bb0bdf83 100644
--- a/main/generated/torch.nn.utils.clip_grads_with_norm_.html
+++ b/main/generated/torch.nn.utils.clip_grads_with_norm_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.clip_grads_with_norm_<a class="headerlink" href="#torch-nn-utils-clip-grads-with-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.clip_grads_with_norm_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grads_with_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/clip_grad.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grads_with_norm_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grads_with_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grads_with_norm_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scale the gradients of an iterable of parameters given a pre-calculated total norm and desired max norm.</p>
 <p>The gradients will be scaled by the following calculation</p>
 <div class="math">
diff --git a/main/generated/torch.nn.utils.convert_conv2d_weight_memory_format.html b/main/generated/torch.nn.utils.convert_conv2d_weight_memory_format.html
index 94f0b38c32e..7ae5dfc8477 100644
--- a/main/generated/torch.nn.utils.convert_conv2d_weight_memory_format.html
+++ b/main/generated/torch.nn.utils.convert_conv2d_weight_memory_format.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.convert_conv2d_weight_memory_format<a class="headerlink" href="#torch-nn-utils-convert-conv2d-weight-memory-format" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.convert_conv2d_weight_memory_format">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">convert_conv2d_weight_memory_format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/memory_format.html#convert_conv2d_weight_memory_format"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/memory_format.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.convert_conv2d_weight_memory_format" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">convert_conv2d_weight_memory_format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/memory_format.html#convert_conv2d_weight_memory_format"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/memory_format.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.convert_conv2d_weight_memory_format" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert <code class="docutils literal notranslate"><span class="pre">memory_format</span></code> of <code class="docutils literal notranslate"><span class="pre">nn.Conv2d.weight</span></code> to <code class="docutils literal notranslate"><span class="pre">memory_format</span></code>.</p>
 <p>The conversion recursively applies to nested <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, including <code class="docutils literal notranslate"><span class="pre">module</span></code>.
 Note that it only changes the memory_format, but not the semantics of each dimensions.
diff --git a/main/generated/torch.nn.utils.convert_conv3d_weight_memory_format.html b/main/generated/torch.nn.utils.convert_conv3d_weight_memory_format.html
index 4a7e28f2571..374c23e588e 100644
--- a/main/generated/torch.nn.utils.convert_conv3d_weight_memory_format.html
+++ b/main/generated/torch.nn.utils.convert_conv3d_weight_memory_format.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.convert_conv3d_weight_memory_format<a class="headerlink" href="#torch-nn-utils-convert-conv3d-weight-memory-format" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.convert_conv3d_weight_memory_format">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">convert_conv3d_weight_memory_format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/memory_format.html#convert_conv3d_weight_memory_format"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/memory_format.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.convert_conv3d_weight_memory_format" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">convert_conv3d_weight_memory_format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/memory_format.html#convert_conv3d_weight_memory_format"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/memory_format.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.convert_conv3d_weight_memory_format" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert <code class="docutils literal notranslate"><span class="pre">memory_format</span></code> of <code class="docutils literal notranslate"><span class="pre">nn.Conv3d.weight</span></code> to <code class="docutils literal notranslate"><span class="pre">memory_format</span></code>
 The conversion recursively applies to nested <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, including <code class="docutils literal notranslate"><span class="pre">module</span></code>.
 Note that it only changes the memory_format, but not the semantics of each dimensions.
diff --git a/main/generated/torch.nn.utils.fuse_conv_bn_eval.html b/main/generated/torch.nn.utils.fuse_conv_bn_eval.html
index 543c1da9d54..60c6cd065d4 100644
--- a/main/generated/torch.nn.utils.fuse_conv_bn_eval.html
+++ b/main/generated/torch.nn.utils.fuse_conv_bn_eval.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.fuse_conv_bn_eval<a class="headerlink" href="#torch-nn-utils-fuse-conv-bn-eval" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.fuse_conv_bn_eval">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_conv_bn_eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_conv_bn_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/fusion.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_conv_bn_eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_conv_bn_eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_conv_bn_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/fusion.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_conv_bn_eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse a convolutional module and a BatchNorm module into a single, new convolutional module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.fuse_conv_bn_weights.html b/main/generated/torch.nn.utils.fuse_conv_bn_weights.html
index f9c1d553252..2fa051b3c8f 100644
--- a/main/generated/torch.nn.utils.fuse_conv_bn_weights.html
+++ b/main/generated/torch.nn.utils.fuse_conv_bn_weights.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.fuse_conv_bn_weights<a class="headerlink" href="#torch-nn-utils-fuse-conv-bn-weights" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.fuse_conv_bn_weights">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_conv_bn_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_conv_bn_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/fusion.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_conv_bn_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_conv_bn_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_conv_bn_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/fusion.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_conv_bn_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse convolutional module parameters and BatchNorm module parameters into new convolutional module parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.fuse_linear_bn_eval.html b/main/generated/torch.nn.utils.fuse_linear_bn_eval.html
index 9840f388472..304d9c66899 100644
--- a/main/generated/torch.nn.utils.fuse_linear_bn_eval.html
+++ b/main/generated/torch.nn.utils.fuse_linear_bn_eval.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.fuse_linear_bn_eval<a class="headerlink" href="#torch-nn-utils-fuse-linear-bn-eval" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.fuse_linear_bn_eval">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_linear_bn_eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_linear_bn_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/fusion.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_linear_bn_eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_linear_bn_eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_linear_bn_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/fusion.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_linear_bn_eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse a linear module and a BatchNorm module into a single, new linear module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.fuse_linear_bn_weights.html b/main/generated/torch.nn.utils.fuse_linear_bn_weights.html
index 9bfa2d796e6..25bc1cbf4c5 100644
--- a/main/generated/torch.nn.utils.fuse_linear_bn_weights.html
+++ b/main/generated/torch.nn.utils.fuse_linear_bn_weights.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.fuse_linear_bn_weights<a class="headerlink" href="#torch-nn-utils-fuse-linear-bn-weights" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.fuse_linear_bn_weights">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_linear_bn_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linear_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_linear_bn_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/fusion.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_linear_bn_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_linear_bn_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linear_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_linear_bn_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/fusion.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_linear_bn_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse linear module parameters and BatchNorm module parameters into new linear module parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.get_total_norm.html b/main/generated/torch.nn.utils.get_total_norm.html
index 523f09d809d..17c1d321b9c 100644
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.get_total_norm<a class="headerlink" href="#torch-nn-utils-get-total-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.get_total_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">get_total_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/clip_grad.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.get_total_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">get_total_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.get_total_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the norm of an iterable of tensors.</p>
 <p>The norm is computed over the norms of the individual tensors, as if the norms of
 the individual tensors were concatenated into a single vector.</p>
diff --git a/main/generated/torch.nn.utils.parameters_to_vector.html b/main/generated/torch.nn.utils.parameters_to_vector.html
index 56d9cfb5cf2..348098feea5 100644
--- a/main/generated/torch.nn.utils.parameters_to_vector.html
+++ b/main/generated/torch.nn.utils.parameters_to_vector.html
@@ -300,7 +300,7 @@
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parameters_to_vector<a class="headerlink" href="#torch-nn-utils-parameters-to-vector" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parameters_to_vector">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">parameters_to_vector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/convert_parameters.html#parameters_to_vector"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/convert_parameters.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parameters_to_vector" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">parameters_to_vector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/convert_parameters.html#parameters_to_vector"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/convert_parameters.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parameters_to_vector" title="Permalink to this definition">¶</a></dt>
 <dd><p>Flatten an iterable of parameters into a single vector.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.parametrizations.orthogonal.html b/main/generated/torch.nn.utils.parametrizations.orthogonal.html
index 4a272afa84c..9f2c706f1c3 100644
--- a/main/generated/torch.nn.utils.parametrizations.orthogonal.html
+++ b/main/generated/torch.nn.utils.parametrizations.orthogonal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrizations.orthogonal<a class="headerlink" href="#torch-nn-utils-parametrizations-orthogonal" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrizations.orthogonal">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">orthogonal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orthogonal_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_trivialization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#orthogonal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/parametrizations.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.orthogonal" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">orthogonal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orthogonal_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_trivialization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#orthogonal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrizations.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.orthogonal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply an orthogonal or unitary parametrization to a matrix or a batch of matrices.</p>
 <p>Letting <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="double-struck">K</mi></mrow><annotation encoding="application/x-tex">\mathbb{K}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6889em;"></span><span class="mord mathbb">K</span></span></span></span></span> be <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="double-struck">R</mi></mrow><annotation encoding="application/x-tex">\mathbb{R}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6889em;"></span><span class="mord mathbb">R</span></span></span></span></span> or <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="double-struck">C</mi></mrow><annotation encoding="application/x-tex">\mathbb{C}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6889em;"></span><span class="mord mathbb">C</span></span></span></span></span>, the parametrized
 matrix <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow><annotation encoding="application/x-tex">Q \in \mathbb{K}^{m \times n}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8778em;vertical-align:-0.1944em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7713em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7713em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span></span></span> is <strong>orthogonal</strong> as</p>
diff --git a/main/generated/torch.nn.utils.parametrizations.spectral_norm.html b/main/generated/torch.nn.utils.parametrizations.spectral_norm.html
index e6b59b1d302..13c44cdb503 100644
--- a/main/generated/torch.nn.utils.parametrizations.spectral_norm.html
+++ b/main/generated/torch.nn.utils.parametrizations.spectral_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrizations.spectral_norm<a class="headerlink" href="#torch-nn-utils-parametrizations-spectral-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrizations.spectral_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_power_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/parametrizations.py#L527"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.spectral_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_power_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrizations.py#L527"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.spectral_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply spectral normalization to a parameter in the given module.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi mathvariant="bold">W</mi><mrow><mi>S</mi><mi>N</mi></mrow></msub><mo>=</mo><mfrac><mi mathvariant="bold">W</mi><mrow><mi>σ</mi><mo stretchy="false">(</mo><mi mathvariant="bold">W</mi><mo stretchy="false">)</mo></mrow></mfrac><mo separator="true">,</mo><mi>σ</mi><mo stretchy="false">(</mo><mi mathvariant="bold">W</mi><mo stretchy="false">)</mo><mo>=</mo><munder><mrow><mi>max</mi><mo>⁡</mo></mrow><mrow><mi mathvariant="bold">h</mi><mo>:</mo><mi mathvariant="bold">h</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></munder><mfrac><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">W</mi><mi mathvariant="bold">h</mi><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub></mrow><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">h</mi><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">\mathbf{W}_{SN} = \dfrac{\mathbf{W}}{\sigma(\mathbf{W})},
diff --git a/main/generated/torch.nn.utils.parametrizations.weight_norm.html b/main/generated/torch.nn.utils.parametrizations.weight_norm.html
index 1bf10619ac4..072ccc70430 100644
--- a/main/generated/torch.nn.utils.parametrizations.weight_norm.html
+++ b/main/generated/torch.nn.utils.parametrizations.weight_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrizations.weight_norm<a class="headerlink" href="#torch-nn-utils-parametrizations-weight-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrizations.weight_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/parametrizations.py#L334"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.weight_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrizations.py#L334"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.weight_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply weight normalization to a parameter in the given module.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi mathvariant="bold">w</mi><mo>=</mo><mi>g</mi><mfrac><mi mathvariant="bold">v</mi><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">v</mi><mi mathvariant="normal">∥</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">\mathbf{w} = g \dfrac{\mathbf{v}}{\|\mathbf{v}\|}
diff --git a/main/generated/torch.nn.utils.parametrize.ParametrizationList.html b/main/generated/torch.nn.utils.parametrize.ParametrizationList.html
index da18615e0a8..5c965d0626e 100644
--- a/main/generated/torch.nn.utils.parametrize.ParametrizationList.html
+++ b/main/generated/torch.nn.utils.parametrize.ParametrizationList.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ParametrizationList<a class="headerlink" href="#parametrizationlist" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.ParametrizationList">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">ParametrizationList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#ParametrizationList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/parametrize.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.ParametrizationList" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">ParametrizationList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#ParametrizationList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.ParametrizationList" title="Permalink to this definition">¶</a></dt>
 <dd><p>A sequential container that holds and manages the original parameters or buffers of a parametrized <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>.</p>
 <p>It is the type of <code class="docutils literal notranslate"><span class="pre">module.parametrizations[tensor_name]</span></code> when <code class="docutils literal notranslate"><span class="pre">module[tensor_name]</span></code>
 has been parametrized with <a class="reference internal" href="torch.nn.utils.parametrize.register_parametrization.html#torch.nn.utils.parametrize.register_parametrization" title="torch.nn.utils.parametrize.register_parametrization"><code class="xref py py-func docutils literal notranslate"><span class="pre">register_parametrization()</span></code></a>.</p>
@@ -640,7 +640,7 @@ <h1>ParametrizationList<a class="headerlink" href="#parametrizationlist" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.ParametrizationList.right_inverse">
-<span class="sig-name descname"><span class="pre">right_inverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#ParametrizationList.right_inverse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/parametrize.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.ParametrizationList.right_inverse" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">right_inverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#ParametrizationList.right_inverse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.ParametrizationList.right_inverse" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call the <code class="docutils literal notranslate"><span class="pre">right_inverse</span></code> methods of the parametrizations in the inverse registration order.</p>
 <p>Then, it stores the result in <code class="docutils literal notranslate"><span class="pre">self.original</span></code> if <code class="docutils literal notranslate"><span class="pre">right_inverse</span></code> outputs one tensor
 or in <code class="docutils literal notranslate"><span class="pre">self.original0</span></code>, <code class="docutils literal notranslate"><span class="pre">self.original1</span></code>, … if it outputs several.</p>
diff --git a/main/generated/torch.nn.utils.parametrize.cached.html b/main/generated/torch.nn.utils.parametrize.cached.html
index fa4acbb1db7..9585d25a70e 100644
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrize.cached<a class="headerlink" href="#torch-nn-utils-parametrize-cached" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.cached">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">cached</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/parametrize.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.cached" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">cached</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.cached" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that enables the caching system within parametrizations registered with <a class="reference internal" href="torch.nn.utils.parametrize.register_parametrization.html#torch.nn.utils.parametrize.register_parametrization" title="torch.nn.utils.parametrize.register_parametrization"><code class="xref py py-func docutils literal notranslate"><span class="pre">register_parametrization()</span></code></a>.</p>
 <p>The value of the parametrized objects is computed and cached the first time
 they are required when this context manager is active. The cached values are
diff --git a/main/generated/torch.nn.utils.parametrize.is_parametrized.html b/main/generated/torch.nn.utils.parametrize.is_parametrized.html
index f5335193236..4d1cc4608ee 100644
--- a/main/generated/torch.nn.utils.parametrize.is_parametrized.html
+++ b/main/generated/torch.nn.utils.parametrize.is_parametrized.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrize.is_parametrized<a class="headerlink" href="#torch-nn-utils-parametrize-is-parametrized" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.is_parametrized">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">is_parametrized</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#is_parametrized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/parametrize.py#L630"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.is_parametrized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">is_parametrized</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#is_parametrized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L630"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.is_parametrized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine if a module has a parametrization.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.parametrize.register_parametrization.html b/main/generated/torch.nn.utils.parametrize.register_parametrization.html
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--- a/main/generated/torch.nn.utils.parametrize.register_parametrization.html
+++ b/main/generated/torch.nn.utils.parametrize.register_parametrization.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrize.register_parametrization<a class="headerlink" href="#torch-nn-utils-parametrize-register-parametrization" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.register_parametrization">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">register_parametrization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parametrization</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#register_parametrization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/parametrize.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.register_parametrization" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">register_parametrization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parametrization</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#register_parametrization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.register_parametrization" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a parametrization to a tensor in a module.</p>
 <p>Assume that <code class="docutils literal notranslate"><span class="pre">tensor_name=&quot;weight&quot;</span></code> for simplicity. When accessing <code class="docutils literal notranslate"><span class="pre">module.weight</span></code>,
 the module will return the parametrized version <code class="docutils literal notranslate"><span class="pre">parametrization(module.weight)</span></code>.
diff --git a/main/generated/torch.nn.utils.parametrize.remove_parametrizations.html b/main/generated/torch.nn.utils.parametrize.remove_parametrizations.html
index e422ca27c2c..d5c7da1ee54 100644
--- a/main/generated/torch.nn.utils.parametrize.remove_parametrizations.html
+++ b/main/generated/torch.nn.utils.parametrize.remove_parametrizations.html
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrize.remove_parametrizations<a class="headerlink" href="#torch-nn-utils-parametrize-remove-parametrizations" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.remove_parametrizations">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">remove_parametrizations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leave_parametrized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#remove_parametrizations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/parametrize.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.remove_parametrizations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">remove_parametrizations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leave_parametrized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#remove_parametrizations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.remove_parametrizations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the parametrizations on a tensor in a module.</p>
 <ul class="simple">
 <li><p>If <code class="docutils literal notranslate"><span class="pre">leave_parametrized=True</span></code>, <code class="docutils literal notranslate"><span class="pre">module[tensor_name]</span></code> will be set to
diff --git a/main/generated/torch.nn.utils.prune.BasePruningMethod.html b/main/generated/torch.nn.utils.prune.BasePruningMethod.html
index aaf9811f569..7e643b73d57 100644
--- a/main/generated/torch.nn.utils.prune.BasePruningMethod.html
+++ b/main/generated/torch.nn.utils.prune.BasePruningMethod.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,13 +612,13 @@
 <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">BasePruningMethod</span></span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">BasePruningMethod</span></span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract base class for creation of new pruning techniques.</p>
 <p>Provides a skeleton for customization requiring the overriding of methods
 such as <a class="reference internal" href="#torch.nn.utils.prune.BasePruningMethod.compute_mask" title="torch.nn.utils.prune.BasePruningMethod.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a> and <a class="reference internal" href="#torch.nn.utils.prune.BasePruningMethod.apply" title="torch.nn.utils.prune.BasePruningMethod.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">apply()</span></code></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -645,7 +645,7 @@ <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.apply_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.apply_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -664,7 +664,7 @@ <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.compute_mask">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.compute_mask" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.compute_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a mask for the input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>Starting from a base <code class="docutils literal notranslate"><span class="pre">default_mask</span></code> (which should be a mask of ones
 if the tensor has not been pruned yet), generate a random mask to
@@ -692,7 +692,7 @@ <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.prune"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.prune"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <a class="reference internal" href="#torch.nn.utils.prune.BasePruningMethod.compute_mask" title="torch.nn.utils.prune.BasePruningMethod.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -719,7 +719,7 @@ <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.remove"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.remove"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.CustomFromMask.html b/main/generated/torch.nn.utils.prune.CustomFromMask.html
index fbd0b77c249..0fb10d63885 100644
--- a/main/generated/torch.nn.utils.prune.CustomFromMask.html
+++ b/main/generated/torch.nn.utils.prune.CustomFromMask.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,10 +612,10 @@
 <h1>CustomFromMask<a class="headerlink" href="#customfrommask" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">CustomFromMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#CustomFromMask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">CustomFromMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#CustomFromMask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#CustomFromMask.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L802"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#CustomFromMask.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L802"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -633,7 +633,7 @@ <h1>CustomFromMask<a class="headerlink" href="#customfrommask" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -652,7 +652,7 @@ <h1>CustomFromMask<a class="headerlink" href="#customfrommask" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code>.</p>
 <dl class="field-list simple">
@@ -679,7 +679,7 @@ <h1>CustomFromMask<a class="headerlink" href="#customfrommask" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.Identity.html b/main/generated/torch.nn.utils.prune.Identity.html
index 5c35ce82652..2935d34ef2c 100644
--- a/main/generated/torch.nn.utils.prune.Identity.html
+++ b/main/generated/torch.nn.utils.prune.Identity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,11 +612,11 @@
 <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">Identity</span></span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#Identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">Identity</span></span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#Identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility pruning method that does not prune any units but generates the pruning parametrization with a mask of ones.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#Identity.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#Identity.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -634,7 +634,7 @@ <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -653,7 +653,7 @@ <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code>.</p>
 <dl class="field-list simple">
@@ -680,7 +680,7 @@ <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.L1Unstructured.html b/main/generated/torch.nn.utils.prune.L1Unstructured.html
index 5f365b1a0da..ea639454b92 100644
--- a/main/generated/torch.nn.utils.prune.L1Unstructured.html
+++ b/main/generated/torch.nn.utils.prune.L1Unstructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">L1Unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#L1Unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">L1Unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#L1Unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune (currently unpruned) units in a tensor by zeroing out the ones with the lowest L1-norm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -624,7 +624,7 @@ <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#L1Unstructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#L1Unstructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -651,7 +651,7 @@ <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -670,7 +670,7 @@ <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code>.</p>
 <dl class="field-list simple">
@@ -697,7 +697,7 @@ <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.LnStructured.html b/main/generated/torch.nn.utils.prune.LnStructured.html
index de98db9efe9..8a34b0d8474 100644
--- a/main/generated/torch.nn.utils.prune.LnStructured.html
+++ b/main/generated/torch.nn.utils.prune.LnStructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">LnStructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L662"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">LnStructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L662"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune entire (currently unpruned) channels in a tensor based on their L<code class="docutils literal notranslate"><span class="pre">n</span></code>-norm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -630,7 +630,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -661,7 +661,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -680,7 +680,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.compute_mask">
-<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.compute_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.compute_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a mask for the input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>Starting from a base <code class="docutils literal notranslate"><span class="pre">default_mask</span></code> (which should be a mask of ones
 if the tensor has not been pruned yet), generate a mask to apply on
@@ -709,7 +709,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <a class="reference internal" href="#torch.nn.utils.prune.LnStructured.compute_mask" title="torch.nn.utils.prune.LnStructured.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -736,7 +736,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.PruningContainer.html b/main/generated/torch.nn.utils.prune.PruningContainer.html
index 66aaf06ddbd..fa37b8a413d 100644
--- a/main/generated/torch.nn.utils.prune.PruningContainer.html
+++ b/main/generated/torch.nn.utils.prune.PruningContainer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">PruningContainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">PruningContainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Container holding a sequence of pruning methods for iterative pruning.</p>
 <p>Keeps track of the order in which pruning methods are applied and handles
 combining successive pruning calls.</p>
@@ -620,7 +620,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 them.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.add_pruning_method">
-<span class="sig-name descname"><span class="pre">add_pruning_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer.add_pruning_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.add_pruning_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_pruning_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer.add_pruning_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.add_pruning_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a child pruning <code class="docutils literal notranslate"><span class="pre">method</span></code> to the container.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -632,7 +632,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -659,7 +659,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -678,7 +678,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.compute_mask">
-<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.compute_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.compute_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the latest <code class="docutils literal notranslate"><span class="pre">method</span></code> by computing the new partial masks and returning its combination with the <code class="docutils literal notranslate"><span class="pre">default_mask</span></code>.</p>
 <p>The new partial mask should be computed on the entries or channels
 that were not zeroed out by the <code class="docutils literal notranslate"><span class="pre">default_mask</span></code>.
@@ -713,7 +713,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <a class="reference internal" href="#torch.nn.utils.prune.PruningContainer.compute_mask" title="torch.nn.utils.prune.PruningContainer.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -740,7 +740,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.RandomStructured.html b/main/generated/torch.nn.utils.prune.RandomStructured.html
index 80a24ee5480..d881542991f 100644
--- a/main/generated/torch.nn.utils.prune.RandomStructured.html
+++ b/main/generated/torch.nn.utils.prune.RandomStructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">RandomStructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">RandomStructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune entire (currently unpruned) channels in a tensor at random.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -628,7 +628,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L640"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L640"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -652,7 +652,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -671,7 +671,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.compute_mask">
-<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.compute_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.compute_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a mask for the input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>Starting from a base <code class="docutils literal notranslate"><span class="pre">default_mask</span></code> (which should be a mask of ones
 if the tensor has not been pruned yet), generate a random mask to
@@ -700,7 +700,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <a class="reference internal" href="#torch.nn.utils.prune.RandomStructured.compute_mask" title="torch.nn.utils.prune.RandomStructured.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -727,7 +727,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.RandomUnstructured.html b/main/generated/torch.nn.utils.prune.RandomUnstructured.html
index 064610e966f..e250c3942ce 100644
--- a/main/generated/torch.nn.utils.prune.RandomUnstructured.html
+++ b/main/generated/torch.nn.utils.prune.RandomUnstructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">RandomUnstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomUnstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L432"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">RandomUnstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomUnstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L432"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune (currently unpruned) units in a tensor at random.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -628,7 +628,7 @@ <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Pe
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomUnstructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomUnstructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -650,7 +650,7 @@ <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -669,7 +669,7 @@ <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code>.</p>
 <dl class="field-list simple">
@@ -696,7 +696,7 @@ <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.custom_from_mask.html b/main/generated/torch.nn.utils.prune.custom_from_mask.html
index 3e8a1d537a1..8fac694c47b 100644
--- a/main/generated/torch.nn.utils.prune.custom_from_mask.html
+++ b/main/generated/torch.nn.utils.prune.custom_from_mask.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.custom_from_mask<a class="headerlink" href="#torch-nn-utils-prune-custom-from-mask" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.custom_from_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">custom_from_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#custom_from_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L1141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.custom_from_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">custom_from_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#custom_from_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L1141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.custom_from_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code> by applying the pre-computed mask in <code class="docutils literal notranslate"><span class="pre">mask</span></code>.</p>
 <p>Modifies module in place (and also return the modified module) by:</p>
 <ol class="arabic simple">
diff --git a/main/generated/torch.nn.utils.prune.global_unstructured.html b/main/generated/torch.nn.utils.prune.global_unstructured.html
index 18788d55423..e6047a7c5b9 100644
--- a/main/generated/torch.nn.utils.prune.global_unstructured.html
+++ b/main/generated/torch.nn.utils.prune.global_unstructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.global_unstructured<a class="headerlink" href="#torch-nn-utils-prune-global-unstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.global_unstructured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">global_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pruning_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#global_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L1022"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.global_unstructured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">global_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pruning_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#global_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L1022"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.global_unstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Globally prunes tensors corresponding to all parameters in <code class="docutils literal notranslate"><span class="pre">parameters</span></code> by applying the specified <code class="docutils literal notranslate"><span class="pre">pruning_method</span></code>.</p>
 <p>Modifies modules in place by:</p>
 <ol class="arabic simple">
diff --git a/main/generated/torch.nn.utils.prune.identity.html b/main/generated/torch.nn.utils.prune.identity.html
index f545137ae92..a218574a44e 100644
--- a/main/generated/torch.nn.utils.prune.identity.html
+++ b/main/generated/torch.nn.utils.prune.identity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.identity<a class="headerlink" href="#torch-nn-utils-prune-identity" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.identity">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.identity" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.identity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply pruning reparametrization without pruning any units.</p>
 <p>Applies pruning reparametrization to the tensor corresponding to the
 parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code> without actually pruning any
diff --git a/main/generated/torch.nn.utils.prune.is_pruned.html b/main/generated/torch.nn.utils.prune.is_pruned.html
index f3c0913a5c2..4d7d2c3f95b 100644
--- a/main/generated/torch.nn.utils.prune.is_pruned.html
+++ b/main/generated/torch.nn.utils.prune.is_pruned.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.is_pruned<a class="headerlink" href="#torch-nn-utils-prune-is-pruned" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.is_pruned">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">is_pruned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#is_pruned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L1204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.is_pruned" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">is_pruned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#is_pruned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L1204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.is_pruned" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if a module is pruned by looking for pruning pre-hooks.</p>
 <p>Check whether <code class="docutils literal notranslate"><span class="pre">module</span></code> is pruned by looking for
 <code class="docutils literal notranslate"><span class="pre">forward_pre_hooks</span></code> in its modules that inherit from the
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+++ b/main/generated/torch.nn.utils.prune.l1_unstructured.html
@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.l1_unstructured<a class="headerlink" href="#torch-nn-utils-prune-l1-unstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.l1_unstructured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">l1_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#l1_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.l1_unstructured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">l1_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#l1_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.l1_unstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor by removing units with the lowest L1-norm.</p>
 <p>Prunes tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code>
 by removing the specified <cite>amount</cite> of (currently unpruned) units with the
diff --git a/main/generated/torch.nn.utils.prune.ln_structured.html b/main/generated/torch.nn.utils.prune.ln_structured.html
index 9af6eba6bd2..06e49a05436 100644
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+++ b/main/generated/torch.nn.utils.prune.ln_structured.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.ln_structured<a class="headerlink" href="#torch-nn-utils-prune-ln-structured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.ln_structured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">ln_structured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#ln_structured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.ln_structured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">ln_structured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#ln_structured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.ln_structured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor by removing channels with the lowest L<code class="docutils literal notranslate"><span class="pre">n</span></code>-norm along the specified dimension.</p>
 <p>Prunes tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code>
 by removing the specified <code class="docutils literal notranslate"><span class="pre">amount</span></code> of (currently unpruned) channels
diff --git a/main/generated/torch.nn.utils.prune.random_structured.html b/main/generated/torch.nn.utils.prune.random_structured.html
index a554acc5885..b15e38fd526 100644
--- a/main/generated/torch.nn.utils.prune.random_structured.html
+++ b/main/generated/torch.nn.utils.prune.random_structured.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.random_structured<a class="headerlink" href="#torch-nn-utils-prune-random-structured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.random_structured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">random_structured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#random_structured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L934"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.random_structured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">random_structured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#random_structured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L934"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.random_structured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor by removing random channels along the specified dimension.</p>
 <p>Prunes tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code>
 by removing the specified <code class="docutils literal notranslate"><span class="pre">amount</span></code> of (currently unpruned) channels
diff --git a/main/generated/torch.nn.utils.prune.random_unstructured.html b/main/generated/torch.nn.utils.prune.random_unstructured.html
index cc53d65cefd..b9f0fb3be5e 100644
--- a/main/generated/torch.nn.utils.prune.random_unstructured.html
+++ b/main/generated/torch.nn.utils.prune.random_unstructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.random_unstructured<a class="headerlink" href="#torch-nn-utils-prune-random-unstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.random_unstructured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">random_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#random_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.random_unstructured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">random_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#random_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.random_unstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor by removing random (currently unpruned) units.</p>
 <p>Prunes tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code>
 by removing the specified <code class="docutils literal notranslate"><span class="pre">amount</span></code> of (currently unpruned) units
diff --git a/main/generated/torch.nn.utils.prune.remove.html b/main/generated/torch.nn.utils.prune.remove.html
index 4780a038297..b40fdb4370f 100644
--- a/main/generated/torch.nn.utils.prune.remove.html
+++ b/main/generated/torch.nn.utils.prune.remove.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.remove<a class="headerlink" href="#torch-nn-utils-prune-remove" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.remove">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#remove"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/prune.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#remove"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module and the pruning method from the forward hook.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned, and the parameter
 named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list. Similarly,
diff --git a/main/generated/torch.nn.utils.remove_spectral_norm.html b/main/generated/torch.nn.utils.remove_spectral_norm.html
index 9a1ccb34acc..7dbda302d1d 100644
--- a/main/generated/torch.nn.utils.remove_spectral_norm.html
+++ b/main/generated/torch.nn.utils.remove_spectral_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.remove_spectral_norm<a class="headerlink" href="#torch-nn-utils-remove-spectral-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.remove_spectral_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">remove_spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/spectral_norm.html#remove_spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/spectral_norm.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.remove_spectral_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">remove_spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/spectral_norm.html#remove_spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/spectral_norm.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.remove_spectral_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the spectral normalization reparameterization from a module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.remove_weight_norm.html b/main/generated/torch.nn.utils.remove_weight_norm.html
index f98e665e2cd..6a23c9d22e1 100644
--- a/main/generated/torch.nn.utils.remove_weight_norm.html
+++ b/main/generated/torch.nn.utils.remove_weight_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.remove_weight_norm<a class="headerlink" href="#torch-nn-utils-remove-weight-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.remove_weight_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">remove_weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/weight_norm.html#remove_weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/weight_norm.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.remove_weight_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">remove_weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/weight_norm.html#remove_weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/weight_norm.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.remove_weight_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the weight normalization reparameterization from a module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.rnn.PackedSequence.html b/main/generated/torch.nn.utils.rnn.PackedSequence.html
index 2051015d069..281f11a5b1b 100644
--- a/main/generated/torch.nn.utils.rnn.PackedSequence.html
+++ b/main/generated/torch.nn.utils.rnn.PackedSequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PackedSequence<a class="headerlink" href="#packedsequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.PackedSequence">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">PackedSequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sorted_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsorted_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/rnn.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">PackedSequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sorted_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsorted_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds the data and list of <a class="reference internal" href="#torch.nn.utils.rnn.PackedSequence.batch_sizes" title="torch.nn.utils.rnn.PackedSequence.batch_sizes"><code class="xref py py-attr docutils literal notranslate"><span class="pre">batch_sizes</span></code></a> of a packed sequence.</p>
 <p>All RNN modules accept packed sequences as inputs.</p>
 <div class="admonition note">
@@ -684,7 +684,7 @@ <h1>PackedSequence<a class="headerlink" href="#packedsequence" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.PackedSequence.is_pinned">
-<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence.is_pinned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/rnn.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence.is_pinned" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence.is_pinned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence.is_pinned" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return true if <cite>self.data</cite> stored on in pinned memory.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -701,7 +701,7 @@ <h1>PackedSequence<a class="headerlink" href="#packedsequence" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.PackedSequence.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/rnn.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence.to" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensor_attributes.html#torch.device" title="torch.device"><span class="pre">device</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span></dt>
 <dt class="sig sig-object py">
diff --git a/main/generated/torch.nn.utils.rnn.pack_padded_sequence.html b/main/generated/torch.nn.utils.rnn.pack_padded_sequence.html
index 7c36ff55aaa..766c082e345 100644
--- a/main/generated/torch.nn.utils.rnn.pack_padded_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.pack_padded_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.pack_padded_sequence<a class="headerlink" href="#torch-nn-utils-rnn-pack-padded-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.pack_padded_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pack_padded_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enforce_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pack_padded_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/rnn.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pack_padded_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pack_padded_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enforce_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pack_padded_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pack_padded_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Packs a Tensor containing padded sequences of variable length.</p>
 <p><code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> can be of size <code class="docutils literal notranslate"><span class="pre">T</span> <span class="pre">x</span> <span class="pre">B</span> <span class="pre">x</span> <span class="pre">*</span></code> (if <code class="xref py py-attr docutils literal notranslate"><span class="pre">batch_first</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code>)
 or <code class="docutils literal notranslate"><span class="pre">B</span> <span class="pre">x</span> <span class="pre">T</span> <span class="pre">x</span> <span class="pre">*</span></code> (if <code class="xref py py-attr docutils literal notranslate"><span class="pre">batch_first</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>) where <code class="docutils literal notranslate"><span class="pre">T</span></code> is the length
diff --git a/main/generated/torch.nn.utils.rnn.pack_sequence.html b/main/generated/torch.nn.utils.rnn.pack_sequence.html
index 4de6228f34f..1ce9c6229b1 100644
--- a/main/generated/torch.nn.utils.rnn.pack_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.pack_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.pack_sequence<a class="headerlink" href="#torch-nn-utils-rnn-pack-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.pack_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pack_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enforce_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pack_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/rnn.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pack_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pack_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enforce_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pack_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pack_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Packs a list of variable length Tensors.</p>
 <p>Consecutive call of the next functions: <code class="docutils literal notranslate"><span class="pre">pad_sequence</span></code>, <code class="docutils literal notranslate"><span class="pre">pack_padded_sequence</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">sequences</span></code> should be a list of Tensors of size <code class="docutils literal notranslate"><span class="pre">L</span> <span class="pre">x</span> <span class="pre">*</span></code>, where <cite>L</cite> is
diff --git a/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html b/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html
index e0983160e1c..4c0ccaa9183 100644
--- a/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.pad_packed_sequence<a class="headerlink" href="#torch-nn-utils-rnn-pad-packed-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.pad_packed_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pad_packed_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequence</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pad_packed_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/rnn.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pad_packed_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pad_packed_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequence</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pad_packed_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pad_packed_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pad a packed batch of variable length sequences.</p>
 <p>It is an inverse operation to <a class="reference internal" href="torch.nn.utils.rnn.pack_padded_sequence.html#torch.nn.utils.rnn.pack_padded_sequence" title="torch.nn.utils.rnn.pack_padded_sequence"><code class="xref py py-func docutils literal notranslate"><span class="pre">pack_padded_sequence()</span></code></a>.</p>
 <p>The returned Tensor’s data will be of size <code class="docutils literal notranslate"><span class="pre">T</span> <span class="pre">x</span> <span class="pre">B</span> <span class="pre">x</span> <span class="pre">*</span></code> (if <code class="xref py py-attr docutils literal notranslate"><span class="pre">batch_first</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code>)
diff --git a/main/generated/torch.nn.utils.rnn.pad_sequence.html b/main/generated/torch.nn.utils.rnn.pad_sequence.html
index 42c629a6075..780e3285bd5 100644
--- a/main/generated/torch.nn.utils.rnn.pad_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.pad_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.pad_sequence<a class="headerlink" href="#torch-nn-utils-rnn-pad-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.pad_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pad_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_side</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'right'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pad_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/rnn.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pad_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pad_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_side</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'right'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pad_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pad_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pad a list of variable length Tensors with <code class="xref py py-attr docutils literal notranslate"><span class="pre">padding_value</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">pad_sequence</span></code> stacks a list of Tensors along a new dimension, and pads them
 to equal length. <code class="xref py py-attr docutils literal notranslate"><span class="pre">sequences</span></code> can be list of sequences with size <code class="docutils literal notranslate"><span class="pre">L</span> <span class="pre">x</span> <span class="pre">*</span></code>,
diff --git a/main/generated/torch.nn.utils.rnn.unpack_sequence.html b/main/generated/torch.nn.utils.rnn.unpack_sequence.html
index 346e3a42b79..148b9e9853d 100644
--- a/main/generated/torch.nn.utils.rnn.unpack_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.unpack_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.unpack_sequence<a class="headerlink" href="#torch-nn-utils-rnn-unpack-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.unpack_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">unpack_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">packed_sequences</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#unpack_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/rnn.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.unpack_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">unpack_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">packed_sequences</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#unpack_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.unpack_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unpack PackedSequence into a list of variable length Tensors.</p>
 <p><code class="docutils literal notranslate"><span class="pre">packed_sequences</span></code> should be a PackedSequence object.</p>
 <p class="rubric">Example</p>
diff --git a/main/generated/torch.nn.utils.rnn.unpad_sequence.html b/main/generated/torch.nn.utils.rnn.unpad_sequence.html
index 44fabf83ec3..2186200a80d 100644
--- a/main/generated/torch.nn.utils.rnn.unpad_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.unpad_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.unpad_sequence<a class="headerlink" href="#torch-nn-utils-rnn-unpad-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.unpad_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">unpad_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padded_sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#unpad_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/rnn.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.unpad_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">unpad_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padded_sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#unpad_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.unpad_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unpad padded Tensor into a list of variable length Tensors.</p>
 <p><code class="docutils literal notranslate"><span class="pre">unpad_sequence</span></code> unstacks padded Tensor into a list of variable length Tensors.</p>
 <p class="rubric">Example</p>
diff --git a/main/generated/torch.nn.utils.skip_init.html b/main/generated/torch.nn.utils.skip_init.html
index d3e29d9fa33..62ac76d3482 100644
--- a/main/generated/torch.nn.utils.skip_init.html
+++ b/main/generated/torch.nn.utils.skip_init.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.skip_init<a class="headerlink" href="#torch-nn-utils-skip-init" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.skip_init">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">skip_init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_cls</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/init.html#skip_init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/init.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.skip_init" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">skip_init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_cls</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/init.html#skip_init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/init.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.skip_init" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a module class object and args / kwargs, instantiate the module without initializing parameters / buffers.</p>
 <p>This can be useful if initialization is slow or if custom initialization will
 be performed, making the default initialization unnecessary. There are some caveats to this, due to
diff --git a/main/generated/torch.nn.utils.spectral_norm.html b/main/generated/torch.nn.utils.spectral_norm.html
index c978784d7dc..36706f7d5b3 100644
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+++ b/main/generated/torch.nn.utils.spectral_norm.html
@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.spectral_norm<a class="headerlink" href="#torch-nn-utils-spectral-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.spectral_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_power_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/spectral_norm.html#spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/spectral_norm.py#L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.spectral_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_power_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/spectral_norm.html#spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/spectral_norm.py#L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.spectral_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply spectral normalization to a parameter in the given module.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi mathvariant="bold">W</mi><mrow><mi>S</mi><mi>N</mi></mrow></msub><mo>=</mo><mfrac><mi mathvariant="bold">W</mi><mrow><mi>σ</mi><mo stretchy="false">(</mo><mi mathvariant="bold">W</mi><mo stretchy="false">)</mo></mrow></mfrac><mo separator="true">,</mo><mi>σ</mi><mo stretchy="false">(</mo><mi mathvariant="bold">W</mi><mo stretchy="false">)</mo><mo>=</mo><munder><mrow><mi>max</mi><mo>⁡</mo></mrow><mrow><mi mathvariant="bold">h</mi><mo>:</mo><mi mathvariant="bold">h</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></munder><mfrac><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">W</mi><mi mathvariant="bold">h</mi><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub></mrow><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">h</mi><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">\mathbf{W}_{SN} = \dfrac{\mathbf{W}}{\sigma(\mathbf{W})},
diff --git a/main/generated/torch.nn.utils.stateless.functional_call.html b/main/generated/torch.nn.utils.stateless.functional_call.html
index 9820a6d4b76..c80265999b6 100644
--- a/main/generated/torch.nn.utils.stateless.functional_call.html
+++ b/main/generated/torch.nn.utils.stateless.functional_call.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.stateless.functional_call<a class="headerlink" href="#torch-nn-utils-stateless-functional-call" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.stateless.functional_call">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.stateless.</span></span><span class="sig-name descname"><span class="pre">functional_call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters_and_buffers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tie_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/stateless.html#functional_call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/stateless.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.stateless.functional_call" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.stateless.</span></span><span class="sig-name descname"><span class="pre">functional_call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters_and_buffers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tie_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/stateless.html#functional_call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/stateless.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.stateless.functional_call" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a functional call on the module by replacing the module parameters and buffers with the provided ones.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.utils.vector_to_parameters.html b/main/generated/torch.nn.utils.vector_to_parameters.html
index 3e4afb82eba..48d9588521b 100644
--- a/main/generated/torch.nn.utils.vector_to_parameters.html
+++ b/main/generated/torch.nn.utils.vector_to_parameters.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.vector_to_parameters<a class="headerlink" href="#torch-nn-utils-vector-to-parameters" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.vector_to_parameters">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">vector_to_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/convert_parameters.html#vector_to_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/convert_parameters.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.vector_to_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">vector_to_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/convert_parameters.html#vector_to_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/convert_parameters.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.vector_to_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy slices of a vector into an iterable of parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.weight_norm.html b/main/generated/torch.nn.utils.weight_norm.html
index d3d7e7e5352..b8b2882e105 100644
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+++ b/main/generated/torch.nn.utils.weight_norm.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.weight_norm<a class="headerlink" href="#torch-nn-utils-weight-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.weight_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/weight_norm.html#weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/utils/weight_norm.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.weight_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/weight_norm.html#weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/weight_norm.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.weight_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply weight normalization to a parameter in the given module.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi mathvariant="bold">w</mi><mo>=</mo><mi>g</mi><mfrac><mi mathvariant="bold">v</mi><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">v</mi><mi mathvariant="normal">∥</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">\mathbf{w} = g \dfrac{\mathbf{v}}{\|\mathbf{v}\|}
diff --git a/main/generated/torch.no_grad.html b/main/generated/torch.no_grad.html
index 106c16871ea..8111352c46b 100644
--- a/main/generated/torch.no_grad.html
+++ b/main/generated/torch.no_grad.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>no_grad<a class="headerlink" href="#no-grad" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.no_grad">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">no_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#no_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/autograd/grad_mode.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.no_grad" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">no_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#no_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.no_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that disables gradient calculation.</p>
 <p>Disabling gradient calculation is useful for inference, when you are sure
 that you will not call <a class="reference internal" href="torch.Tensor.backward.html#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.backward()</span></code></a>. It will reduce memory
diff --git a/main/generated/torch.nonzero.html b/main/generated/torch.nonzero.html
index 32753a7cefe..936a33b6637 100644
--- a/main/generated/torch.nonzero.html
+++ b/main/generated/torch.nonzero.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
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diff --git a/main/generated/torch.norm.html b/main/generated/torch.norm.html
index b247d7d7f2b..f039f0434aa 100644
--- a/main/generated/torch.norm.html
+++ b/main/generated/torch.norm.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.norm<a class="headerlink" href="#torch-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.norm">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L1677"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1677"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the matrix norm or vector norm of a given tensor.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.normal.html b/main/generated/torch.normal.html
index a06769e7f72..e6d83432fcc 100644
--- a/main/generated/torch.normal.html
+++ b/main/generated/torch.normal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.not_equal.html b/main/generated/torch.not_equal.html
index b078b2b7817..650f72e0060 100644
--- a/main/generated/torch.not_equal.html
+++ b/main/generated/torch.not_equal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.numel.html b/main/generated/torch.numel.html
index 3a4f2cf241c..2982f124100 100644
--- a/main/generated/torch.numel.html
+++ b/main/generated/torch.numel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ones.html b/main/generated/torch.ones.html
index f3ffc7138e0..a3eb1cb3139 100644
--- a/main/generated/torch.ones.html
+++ b/main/generated/torch.ones.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ones_like.html b/main/generated/torch.ones_like.html
index 7c68d5b9d0d..7c6b7dd1a0d 100644
--- a/main/generated/torch.ones_like.html
+++ b/main/generated/torch.ones_like.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.onnx.JitScalarType.html b/main/generated/torch.onnx.JitScalarType.html
index 337a74de7f8..e1c9218c7e1 100644
--- a/main/generated/torch.onnx.JitScalarType.html
+++ b/main/generated/torch.onnx.JitScalarType.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -632,7 +632,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.dtype">
-<span class="sig-name descname"><span class="pre">dtype</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_type_utils.py#L253"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.dtype" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">dtype</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L253"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.dtype" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a JitScalarType to a torch dtype.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -643,7 +643,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.from_dtype">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_type_utils.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_dtype" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_dtype" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a torch dtype to JitScalarType.</p>
 <dl class="simple">
 <dt>Note: DO NOT USE this API when <cite>dtype</cite> comes from a <cite>torch._C.Value.type()</cite> calls.</dt><dd><p>A “RuntimeError: INTERNAL ASSERT FAILED at “../aten/src/ATen/core/jit_type_base.h” can
@@ -669,7 +669,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.from_onnx_type">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_onnx_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">onnx_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_onnx_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_type_utils.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_onnx_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_onnx_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">onnx_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_onnx_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_onnx_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a ONNX data type to JitScalarType.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -689,7 +689,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.from_value">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_type_utils.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_value" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_value" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a JitScalarType from an value’s scalar type.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -715,7 +715,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.onnx_compatible">
-<span class="sig-name descname"><span class="pre">onnx_compatible</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.onnx_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_type_utils.py#L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.onnx_compatible" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">onnx_compatible</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.onnx_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.onnx_compatible" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether this JitScalarType is compatible with ONNX.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -726,7 +726,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.onnx_type">
-<span class="sig-name descname"><span class="pre">onnx_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.onnx_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_type_utils.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.onnx_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">onnx_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.onnx_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.onnx_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a JitScalarType to an ONNX data type.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -737,7 +737,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.scalar_name">
-<span class="sig-name descname"><span class="pre">scalar_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.scalar_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_type_utils.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.scalar_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">scalar_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.scalar_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.scalar_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a JitScalarType to a JIT scalar type name.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -748,7 +748,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.torch_name">
-<span class="sig-name descname"><span class="pre">torch_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.torch_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_type_utils.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.torch_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">torch_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.torch_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.torch_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a JitScalarType to a torch type name.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.onnx.verification.GraphInfo.html b/main/generated/torch.onnx.verification.GraphInfo.html
index 6da37fe7022..de7866094f9 100644
--- a/main/generated/torch.onnx.verification.GraphInfo.html
+++ b/main/generated/torch.onnx.verification.GraphInfo.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,13 +614,13 @@
 <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">GraphInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_EXCLUDED_NODE_KINDS=frozenset({'aten::ScalarImplicit'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'prim::Constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'prim::ListConstruct'})</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">GraphInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_EXCLUDED_NODE_KINDS=frozenset({'aten::ScalarImplicit'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'prim::Constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'prim::ListConstruct'})</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo" title="Permalink to this definition">¶</a></dt>
 <dd><p>GraphInfo contains validation information of a TorchScript graph and its converted ONNX graph.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.all_mismatch_leaf_graph_info">
-<span class="sig-name descname"><span class="pre">all_mismatch_leaf_graph_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.all_mismatch_leaf_graph_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.all_mismatch_leaf_graph_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">all_mismatch_leaf_graph_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.all_mismatch_leaf_graph_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.all_mismatch_leaf_graph_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of all leaf <cite>GraphInfo</cite> objects that have mismatch.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -631,13 +631,13 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.clear">
-<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.clear" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.clear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clear states and results of previous verification.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.essential_node_count">
-<span class="sig-name descname"><span class="pre">essential_node_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.essential_node_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.essential_node_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">essential_node_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.essential_node_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.essential_node_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of nodes in the subgraph excluding those in <cite>_EXCLUDED_NODE_KINDS</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -648,7 +648,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.essential_node_kinds">
-<span class="sig-name descname"><span class="pre">essential_node_kinds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.essential_node_kinds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.essential_node_kinds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">essential_node_kinds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.essential_node_kinds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.essential_node_kinds" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the set of node kinds in the subgraph excluding those in <cite>_EXCLUDED_NODE_KINDS</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -659,7 +659,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.export_repro">
-<span class="sig-name descname"><span class="pre">export_repro</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">repro_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.export_repro"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.export_repro" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">export_repro</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">repro_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.export_repro"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.export_repro" title="Permalink to this definition">¶</a></dt>
 <dd><p>Export the subgraph to ONNX along with the input/output data for repro.</p>
 <p>The repro directory will contain the following files:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span>dir
@@ -691,7 +691,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.find_mismatch">
-<span class="sig-name descname"><span class="pre">find_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.find_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1576"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.find_mismatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">find_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.find_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1576"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.find_mismatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find all mismatches between the TorchScript IR graph and the exported onnx model.</p>
 <p>Binary searches the model graph to find the minimal subgraph that exhibits the
 mismatch. A <cite>GraphInfo</cite> object is created for each subgraph, recording the test
@@ -705,7 +705,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.find_partition">
-<span class="sig-name descname"><span class="pre">find_partition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.find_partition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.find_partition" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">find_partition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.find_partition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.find_partition" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find the <cite>GraphInfo</cite> object with the given id.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -716,7 +716,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.has_mismatch">
-<span class="sig-name descname"><span class="pre">has_mismatch</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.has_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.has_mismatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">has_mismatch</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.has_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.has_mismatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if the subgraph has output mismatch between torch and ONNX.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -727,7 +727,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.pretty_print_mismatch">
-<span class="sig-name descname"><span class="pre">pretty_print_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.pretty_print_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.pretty_print_mismatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pretty_print_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.pretty_print_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.pretty_print_mismatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pretty print details of the mismatch between torch and ONNX.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -738,7 +738,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.pretty_print_tree">
-<span class="sig-name descname"><span class="pre">pretty_print_tree</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.pretty_print_tree"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.pretty_print_tree" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pretty_print_tree</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.pretty_print_tree"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.pretty_print_tree" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pretty print <cite>GraphInfo</cite> tree.</p>
 <p>Each node represents a subgraph, showing the number of nodes in the subgraph and
 a check mark if the subgraph has output mismatch between torch and ONNX.</p>
@@ -770,7 +770,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.verify_export">
-<span class="sig-name descname"><span class="pre">verify_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">options</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.verify_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1547"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.verify_export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">verify_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">options</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.verify_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1547"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.verify_export" title="Permalink to this definition">¶</a></dt>
 <dd><p>Verify the export from TorchScript IR graph to ONNX.</p>
 <p>Export the TorchScript IR graph to ONNX, with the inputs, parameters and export
 options recorded in this object. Then verify the exported ONNX graph against
diff --git a/main/generated/torch.onnx.verification.VerificationOptions.html b/main/generated/torch.onnx.verification.VerificationOptions.html
index b2aa73436f2..d5818af3141 100644
--- a/main/generated/torch.onnx.verification.VerificationOptions.html
+++ b/main/generated/torch.onnx.verification.VerificationOptions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>VerificationOptions<a class="headerlink" href="#verificationoptions" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.onnx.verification.VerificationOptions">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">VerificationOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OnnxBackend.ONNX_RUNTIME_CPU</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remained_onnx_input_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acceptable_error_percentage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#VerificationOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.VerificationOptions" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">VerificationOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OnnxBackend.ONNX_RUNTIME_CPU</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remained_onnx_input_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acceptable_error_percentage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#VerificationOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.VerificationOptions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Options for ONNX export verification.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Variables</dt>
diff --git a/main/generated/torch.optim.ASGD.html b/main/generated/torch.optim.ASGD.html
index 2e4009fb18b..c05162ce9ca 100644
--- a/main/generated/torch.optim.ASGD.html
+++ b/main/generated/torch.optim.ASGD.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.ASGD">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">ASGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/asgd.html#ASGD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/asgd.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">ASGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/asgd.html#ASGD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/asgd.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Averaged Stochastic Gradient Descent.</p>
 <p>It has been proposed in <a class="reference external" href="https://dl.acm.org/citation.cfm?id=131098">Acceleration of stochastic approximation by
 averaging</a>.</p>
@@ -650,7 +650,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -664,7 +664,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -688,7 +688,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -722,7 +722,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -759,7 +759,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -791,7 +791,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -823,7 +823,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -846,7 +846,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -871,7 +871,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -934,7 +934,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/asgd.html#ASGD.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/asgd.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/asgd.html#ASGD.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/asgd.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -946,7 +946,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adadelta.html b/main/generated/torch.optim.Adadelta.html
index 4dd59aa1e7a..1e6f4eff0ea 100644
--- a/main/generated/torch.optim.Adadelta.html
+++ b/main/generated/torch.optim.Adadelta.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Adadelta">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adadelta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adadelta.html#Adadelta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adadelta.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adadelta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adadelta.html#Adadelta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adadelta.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Adadelta algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>ρ</mi><mtext> (decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> </mtext><mtext> (square avg)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>u</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> </mtext><mtext> (accumulate variables)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mi>ρ</mi><mo>+</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>ρ</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi mathvariant="normal">Δ</mi><msub><mi>x</mi><mi>t</mi></msub><mo>←</mo><mfrac><msqrt><mrow><msub><mi>u</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mi>ϵ</mi></mrow></msqrt><msqrt><mrow><msub><mi>v</mi><mi>t</mi></msub><mo>+</mo><mi>ϵ</mi></mrow></msqrt></mfrac><msub><mi>g</mi><mi>t</mi></msub><mspace width="5.9751em"/></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>u</mi><mi>t</mi></msub><mo>←</mo><msub><mi>u</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mi>ρ</mi><mo>+</mo><mi mathvariant="normal">Δ</mi><msubsup><mi>x</mi><mi>t</mi><mn>2</mn></msubsup><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>ρ</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><mi mathvariant="normal">Δ</mi><msub><mi>x</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -699,7 +699,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -713,7 +713,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -737,7 +737,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -771,7 +771,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -808,7 +808,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -840,7 +840,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -872,7 +872,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -895,7 +895,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -920,7 +920,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -983,7 +983,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adadelta.html#Adadelta.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adadelta.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adadelta.html#Adadelta.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adadelta.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -995,7 +995,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adafactor.html b/main/generated/torch.optim.Adafactor.html
index a0807d5f6fe..a5dab0df1b7 100644
--- a/main/generated/torch.optim.Adafactor.html
+++ b/main/generated/torch.optim.Adafactor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -839,7 +839,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 M1001 80h400000v40h-400000z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.1683em;"><span></span></span></span></span></span></span></span><span style="top:-3.3017em;"><span class="pstrut" style="height:3.0717em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.7487em;"><span class="pstrut" style="height:3.0717em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal">G</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">t</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:1.13em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:4.0363em;"><span></span></span></span></span></span></span></span></span></span></span></span></div></div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -853,7 +853,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -877,7 +877,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -911,7 +911,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -948,7 +948,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -980,7 +980,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -1012,7 +1012,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -1035,7 +1035,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -1060,7 +1060,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1123,7 +1123,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/_adafactor.html#Adafactor.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/_adafactor.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/_adafactor.html#Adafactor.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/_adafactor.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1135,7 +1135,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adagrad.html b/main/generated/torch.optim.Adagrad.html
index 0a90337bff4..9bb14a18b8b 100644
--- a/main/generated/torch.optim.Adagrad.html
+++ b/main/generated/torch.optim.Adagrad.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Adagrad">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adagrad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_accumulator_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adagrad.html#Adagrad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adagrad.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adagrad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_accumulator_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adagrad.html#Adagrad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adagrad.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Adagrad algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><mi>τ</mi><mtext> (initial accumulator value)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>η</mi><mtext> (lr decay)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><mi mathvariant="normal">_</mi><mi>s</mi><mi>u</mi><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mi>τ</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><mi>γ</mi><mo>~</mo></mover><mo>←</mo><mi>γ</mi><mi mathvariant="normal">/</mi><mo stretchy="false">(</mo><mn>1</mn><mo>+</mo><mo stretchy="false">(</mo><mi>t</mi><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mi>η</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><mi mathvariant="normal">_</mi><mi>s</mi><mi>u</mi><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><mi mathvariant="normal">_</mi><mi>s</mi><mi>u</mi><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mover accent="true"><mi>γ</mi><mo>~</mo></mover><mfrac><msub><mi>g</mi><mi>t</mi></msub><mrow><msqrt><mrow><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><mi mathvariant="normal">_</mi><mi>s</mi><mi>u</mi><msub><mi>m</mi><mi>t</mi></msub></mrow></msqrt><mo>+</mo><mi>ϵ</mi></mrow></mfrac></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -683,7 +683,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -697,7 +697,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -721,7 +721,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -755,7 +755,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -792,7 +792,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -824,7 +824,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -856,7 +856,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -879,7 +879,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -904,7 +904,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -967,7 +967,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adagrad.html#Adagrad.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adagrad.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adagrad.html#Adagrad.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adagrad.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -979,7 +979,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adam.html b/main/generated/torch.optim.Adam.html
index ebf0c954362..4506dc47e1d 100644
--- a/main/generated/torch.optim.Adam.html
+++ b/main/generated/torch.optim.Adam.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Adam">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amsgrad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adam.html#Adam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adam.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amsgrad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adam.html#Adam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adam.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Adam algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext> (betas)</mtext><mo separator="true">,</mo><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">amsgrad</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo separator="true">,</mo><mtext> </mtext><mi>ϵ</mi><mtext> (epsilon)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( first moment)</mtext><mo separator="true">,</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> (second moment)</mtext><mo separator="true">,</mo><mtext> </mtext><msubsup><mi>v</mi><mn>0</mn><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo>←</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo>:</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><mo>−</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>m</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>1</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>a</mi><mi>m</mi><mi>s</mi><mi>g</mi><mi>r</mi><mi>a</mi><mi>d</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msubsup><mi>v</mi><mi>t</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow><mo stretchy="false">(</mo><msubsup><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo separator="true">,</mo><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msubsup><mi>v</mi><mi>t</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>v</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -722,7 +722,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -736,7 +736,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -760,7 +760,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -794,7 +794,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -831,7 +831,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -863,7 +863,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -895,7 +895,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -918,7 +918,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -943,7 +943,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1006,7 +1006,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adam.html#Adam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adam.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adam.html#Adam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adam.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1018,7 +1018,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.AdamW.html b/main/generated/torch.optim.AdamW.html
index d7ca7b11ab3..ef1d5d7aa38 100644
--- a/main/generated/torch.optim.AdamW.html
+++ b/main/generated/torch.optim.AdamW.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.AdamW">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">AdamW</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amsgrad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamw.html#AdamW"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adamw.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">AdamW</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amsgrad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamw.html#AdamW"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adamw.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements AdamW algorithm, where weight decay does not accumulate in the momentum nor variance.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext>(lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext>(betas)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext>(params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext>(objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>ϵ</mi><mtext> (epsilon)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>λ</mi><mtext>(weight decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">amsgrad</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> (first moment)</mtext><mo separator="true">,</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( second moment)</mtext><mo separator="true">,</mo><mtext> </mtext><msubsup><mi>v</mi><mn>0</mn><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo>←</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo>:</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><mo>−</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>m</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>1</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>a</mi><mi>m</mi><mi>s</mi><mi>g</mi><mi>r</mi><mi>a</mi><mi>d</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msubsup><mi>v</mi><mi>t</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow><mo stretchy="false">(</mo><msubsup><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo separator="true">,</mo><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msubsup><mi>v</mi><mi>t</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>v</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -719,7 +719,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -733,7 +733,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -757,7 +757,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -791,7 +791,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -828,7 +828,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -860,7 +860,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -892,7 +892,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -915,7 +915,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -940,7 +940,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1003,7 +1003,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adam.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adam.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1015,7 +1015,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adamax.html b/main/generated/torch.optim.Adamax.html
index 3ed8ec1093d..c74bccebf0e 100644
--- a/main/generated/torch.optim.Adamax.html
+++ b/main/generated/torch.optim.Adamax.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Adamax">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adamax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.002</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamax.html#Adamax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adamax.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adamax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.002</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamax.html#Adamax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adamax.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Adamax algorithm (a variant of Adam based on infinity norm).</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext> (betas)</mtext><mo separator="true">,</mo><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>ϵ</mi><mtext> (epsilon)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( first moment)</mtext><mo separator="true">,</mo><msub><mi>u</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( infinity norm)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>u</mi><mi>t</mi></msub><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow><mo stretchy="false">(</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>u</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo separator="true">,</mo><mi mathvariant="normal">∣</mi><msub><mi>g</mi><mi>t</mi></msub><mi mathvariant="normal">∣</mi><mo>+</mo><mi>ϵ</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mfrac><mrow><mi>γ</mi><msub><mi>m</mi><mi>t</mi></msub></mrow><mrow><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>1</mn><mi>t</mi></msubsup><mo stretchy="false">)</mo><msub><mi>u</mi><mi>t</mi></msub></mrow></mfrac></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -672,7 +672,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -686,7 +686,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -710,7 +710,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -744,7 +744,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -781,7 +781,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -813,7 +813,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -845,7 +845,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -868,7 +868,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -893,7 +893,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -956,7 +956,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamax.html#Adamax.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/adamax.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamax.html#Adamax.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adamax.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -968,7 +968,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.LBFGS.html b/main/generated/torch.optim.LBFGS.html
index b3efa0c3174..b8ad7057509 100644
--- a/main/generated/torch.optim.LBFGS.html
+++ b/main/generated/torch.optim.LBFGS.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.LBFGS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">LBFGS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_eval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-09</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_search_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lbfgs.html#LBFGS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lbfgs.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">LBFGS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_eval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-09</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_search_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lbfgs.html#LBFGS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lbfgs.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements L-BFGS algorithm.</p>
 <p>Heavily inspired by <a class="reference external" href="https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html">minFunc</a>.</p>
 <div class="admonition warning">
@@ -651,7 +651,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -665,7 +665,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -689,7 +689,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -723,7 +723,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -760,7 +760,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -792,7 +792,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -824,7 +824,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -847,7 +847,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -872,7 +872,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -935,7 +935,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lbfgs.html#LBFGS.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lbfgs.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lbfgs.html#LBFGS.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lbfgs.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -947,7 +947,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.NAdam.html b/main/generated/torch.optim.NAdam.html
index 60a36712ff7..72092d12039 100644
--- a/main/generated/torch.optim.NAdam.html
+++ b/main/generated/torch.optim.NAdam.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.NAdam">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">NAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.002</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.004</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/nadam.html#NAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/nadam.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">NAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.002</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.004</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/nadam.html#NAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/nadam.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements NAdam algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><msub><mi>γ</mi><mi>t</mi></msub><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext> (betas)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>ψ</mi><mtext> (momentum decay)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mtext> </mtext><mtext mathvariant="italic">decoupled_weight_decay</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( first moment)</mtext><mo separator="true">,</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( second moment)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo>:</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><mo>−</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">decoupled_weight_decay</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>μ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><mfrac><mn>1</mn><mn>2</mn></mfrac><msup><mn>0.96</mn><mrow><mi>t</mi><mi>ψ</mi></mrow></msup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>μ</mi><mrow><mi>t</mi><mo>+</mo><mn>1</mn></mrow></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><mfrac><mn>1</mn><mn>2</mn></mfrac><msup><mn>0.96</mn><mrow><mo stretchy="false">(</mo><mi>t</mi><mo>+</mo><mn>1</mn><mo stretchy="false">)</mo><mi>ψ</mi></mrow></msup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>μ</mi><mrow><mi>t</mi><mo>+</mo><mn>1</mn></mrow></msub><msub><mi>m</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><munderover><mo>∏</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>t</mi><mo>+</mo><mn>1</mn></mrow></munderover><msub><mi>μ</mi><mi>i</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.1298em"/><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>μ</mi><mi>t</mi></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><munderover><mo>∏</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>t</mi></munderover><msub><mi>μ</mi><mi>i</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>v</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -703,7 +703,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -717,7 +717,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -741,7 +741,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -775,7 +775,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -812,7 +812,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -844,7 +844,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -876,7 +876,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -899,7 +899,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -924,7 +924,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -987,7 +987,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/nadam.html#NAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/nadam.py#L154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/nadam.html#NAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/nadam.py#L154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -999,7 +999,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Optimizer.add_param_group.html b/main/generated/torch.optim.Optimizer.add_param_group.html
index 0f39895d0da..7a287e53dc7 100644
--- a/main/generated/torch.optim.Optimizer.add_param_group.html
+++ b/main/generated/torch.optim.Optimizer.add_param_group.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.add_param_group<a class="headerlink" href="#torch-optim-optimizer-add-param-group" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.add_param_group">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.add_param_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.add_param_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
diff --git a/main/generated/torch.optim.Optimizer.load_state_dict.html b/main/generated/torch.optim.Optimizer.load_state_dict.html
index d9fa6d1c03c..366920e5d92 100644
--- a/main/generated/torch.optim.Optimizer.load_state_dict.html
+++ b/main/generated/torch.optim.Optimizer.load_state_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.load_state_dict<a class="headerlink" href="#torch-optim-optimizer-load-state-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.load_state_dict">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Optimizer.register_load_state_dict_post_hook.html b/main/generated/torch.optim.Optimizer.register_load_state_dict_post_hook.html
index a578d54fef4..41131aab9b2 100644
--- a/main/generated/torch.optim.Optimizer.register_load_state_dict_post_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_load_state_dict_post_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_load_state_dict_post_hook<a class="headerlink" href="#torch-optim-optimizer-register-load-state-dict-post-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_load_state_dict_post_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_load_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_load_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
diff --git a/main/generated/torch.optim.Optimizer.register_load_state_dict_pre_hook.html b/main/generated/torch.optim.Optimizer.register_load_state_dict_pre_hook.html
index 0ec0278e781..ffe0ddc73ff 100644
--- a/main/generated/torch.optim.Optimizer.register_load_state_dict_pre_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_load_state_dict_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_load_state_dict_pre_hook<a class="headerlink" href="#torch-optim-optimizer-register-load-state-dict-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_load_state_dict_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_load_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_load_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
diff --git a/main/generated/torch.optim.Optimizer.register_state_dict_post_hook.html b/main/generated/torch.optim.Optimizer.register_state_dict_post_hook.html
index 092117bce1a..7c4cb0d2c55 100644
--- a/main/generated/torch.optim.Optimizer.register_state_dict_post_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_state_dict_post_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_state_dict_post_hook<a class="headerlink" href="#torch-optim-optimizer-register-state-dict-post-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_state_dict_post_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
diff --git a/main/generated/torch.optim.Optimizer.register_state_dict_pre_hook.html b/main/generated/torch.optim.Optimizer.register_state_dict_pre_hook.html
index f3eca0e1523..594a619895d 100644
--- a/main/generated/torch.optim.Optimizer.register_state_dict_pre_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_state_dict_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_state_dict_pre_hook<a class="headerlink" href="#torch-optim-optimizer-register-state-dict-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_state_dict_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
diff --git a/main/generated/torch.optim.Optimizer.register_step_post_hook.html b/main/generated/torch.optim.Optimizer.register_step_post_hook.html
index ab437a574a3..baa3b4a21ba 100644
--- a/main/generated/torch.optim.Optimizer.register_step_post_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_step_post_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_step_post_hook<a class="headerlink" href="#torch-optim-optimizer-register-step-post-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_step_post_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_step_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_step_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
diff --git a/main/generated/torch.optim.Optimizer.register_step_pre_hook.html b/main/generated/torch.optim.Optimizer.register_step_pre_hook.html
index 344cc53ee35..ab64b906d26 100644
--- a/main/generated/torch.optim.Optimizer.register_step_pre_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_step_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_step_pre_hook<a class="headerlink" href="#torch-optim-optimizer-register-step-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_step_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_step_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_step_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
diff --git a/main/generated/torch.optim.Optimizer.state_dict.html b/main/generated/torch.optim.Optimizer.state_dict.html
index 34caf1d510a..5c641320bfe 100644
--- a/main/generated/torch.optim.Optimizer.state_dict.html
+++ b/main/generated/torch.optim.Optimizer.state_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.state_dict<a class="headerlink" href="#torch-optim-optimizer-state-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.state_dict">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
diff --git a/main/generated/torch.optim.Optimizer.step.html b/main/generated/torch.optim.Optimizer.step.html
index 2fabfb438f7..7a80d111d3f 100644
--- a/main/generated/torch.optim.Optimizer.step.html
+++ b/main/generated/torch.optim.Optimizer.step.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.step<a class="headerlink" href="#torch-optim-optimizer-step" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.step">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L994"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L994"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.step" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></dt>
 <dd><p>Perform a single optimization step to update parameter.</p>
diff --git a/main/generated/torch.optim.Optimizer.zero_grad.html b/main/generated/torch.optim.Optimizer.zero_grad.html
index 60cfd1c6349..946443e36f5 100644
--- a/main/generated/torch.optim.Optimizer.zero_grad.html
+++ b/main/generated/torch.optim.Optimizer.zero_grad.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.zero_grad<a class="headerlink" href="#torch-optim-optimizer-zero-grad" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.zero_grad">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.RAdam.html b/main/generated/torch.optim.RAdam.html
index a1ad2d9a874..088508e6340 100644
--- a/main/generated/torch.optim.RAdam.html
+++ b/main/generated/torch.optim.RAdam.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.RAdam">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">RAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/radam.html#RAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/radam.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">RAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/radam.html#RAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/radam.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements RAdam algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext> (betas)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weightdecay)</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>ϵ</mi><mtext> (epsilon)</mtext><mo separator="true">,</mo><mtext mathvariant="italic">decoupled_weight_decay</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( first moment)</mtext><mo separator="true">,</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( second moment)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="5.1215em"/><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub><mo>←</mo><mn>2</mn><mi mathvariant="normal">/</mi><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo>:</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><mo>−</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">decoupled_weight_decay</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="5.1215em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mi>λ</mi><msub><mi>θ</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="5.1215em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>m</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>1</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><msub><mi>ρ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub><mo>−</mo><mn>2</mn><mi>t</mi><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><msub><mi>ρ</mi><mi>t</mi></msub><mo>&gt;</mo><mn>5</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>l</mi><mi>t</mi></msub><mo>←</mo><mfrac><msqrt><mrow><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo stretchy="false">)</mo></mrow></msqrt><mrow><msqrt><msub><mi>v</mi><mi>t</mi></msub></msqrt><mo>+</mo><mi>ϵ</mi></mrow></mfrac></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>r</mi><mi>t</mi></msub><mo>←</mo><msqrt><mfrac><mrow><mo stretchy="false">(</mo><msub><mi>ρ</mi><mi>t</mi></msub><mo>−</mo><mn>4</mn><mo stretchy="false">)</mo><mo stretchy="false">(</mo><msub><mi>ρ</mi><mi>t</mi></msub><mo>−</mo><mn>2</mn><mo stretchy="false">)</mo><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub></mrow><mrow><mo stretchy="false">(</mo><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub><mo>−</mo><mn>4</mn><mo stretchy="false">)</mo><mo stretchy="false">(</mo><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub><mo>−</mo><mn>2</mn><mo stretchy="false">)</mo><msub><mi>ρ</mi><mi>t</mi></msub></mrow></mfrac></msqrt></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><msub><mi>r</mi><mi>t</mi></msub><msub><mi>l</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -729,7 +729,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -743,7 +743,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -767,7 +767,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -801,7 +801,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -838,7 +838,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -870,7 +870,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -902,7 +902,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -925,7 +925,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -950,7 +950,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1013,7 +1013,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/radam.html#RAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/radam.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/radam.html#RAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/radam.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1025,7 +1025,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.RMSprop.html b/main/generated/torch.optim.RMSprop.html
index 5dafcdafefa..041f6887a8f 100644
--- a/main/generated/torch.optim.RMSprop.html
+++ b/main/generated/torch.optim.RMSprop.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.RMSprop">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">RMSprop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rmsprop.html#RMSprop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/rmsprop.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">RMSprop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rmsprop.html#RMSprop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/rmsprop.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements RMSprop algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>α</mi><mtext> (alpha)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>μ</mi><mtext> (momentum)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>c</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>e</mi><mi>r</mi><mi>e</mi><mi>d</mi><mo separator="true">,</mo><mtext> </mtext><mi>ϵ</mi><mtext> (epsilon)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> (square average)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mtext mathvariant="bold">b</mtext><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> (buffer)</mtext><mo separator="true">,</mo><mtext> </mtext><msubsup><mi>g</mi><mn>0</mn><mrow><mi>a</mi><mi>v</mi><mi>e</mi></mrow></msubsup><mo>←</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><mi>α</mi><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup><mspace width="2.2762em"/></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover><mo>←</mo><msub><mi>v</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>c</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>e</mi><mi>r</mi><mi>e</mi><mi>d</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msubsup><mi>g</mi><mi>t</mi><mrow><mi>a</mi><mi>v</mi><mi>e</mi></mrow></msubsup><mo>←</mo><msubsup><mi>g</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mrow><mi>a</mi><mi>v</mi><mi>e</mi></mrow></msubsup><mi>α</mi><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover><mo>←</mo><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover><mo>−</mo><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msubsup><mi>g</mi><mi>t</mi><mrow><mi>a</mi><mi>v</mi><mi>e</mi></mrow></msubsup><msup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo><mn>2</mn></msup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>μ</mi><mo>&gt;</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub><mo>←</mo><mi>μ</mi><msub><mtext mathvariant="bold">b</mtext><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><msub><mi>g</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>e</mi><mi>l</mi><mi>s</mi><mi>e</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><msub><mi>g</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo><mspace width="0.8536em"/></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -723,7 +723,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -737,7 +737,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -761,7 +761,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -795,7 +795,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -832,7 +832,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -864,7 +864,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -896,7 +896,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -919,7 +919,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -944,7 +944,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1007,7 +1007,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rmsprop.html#RMSprop.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/rmsprop.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rmsprop.html#RMSprop.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/rmsprop.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1019,7 +1019,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Rprop.html b/main/generated/torch.optim.Rprop.html
index a95e296d147..7368cc33b5a 100644
--- a/main/generated/torch.optim.Rprop.html
+++ b/main/generated/torch.optim.Rprop.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Rprop">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Rprop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">etas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">1.2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-06,</span> <span class="pre">50)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rprop.html#Rprop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/rprop.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Rprop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">etas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">1.2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-06,</span> <span class="pre">50)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rprop.html#Rprop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/rprop.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements the resilient backpropagation algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><msub><mi>θ</mi><mn>0</mn></msub><mo>∈</mo><msup><mi mathvariant="bold">R</mi><mi>d</mi></msup><mtext> (params)</mtext><mo separator="true">,</mo><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><msub><mi>η</mi><mrow><mo>+</mo><mi mathvariant="normal">/</mi><mo>−</mo></mrow></msub><mtext> (etaplus, etaminus)</mtext><mo separator="true">,</mo><msub><mi mathvariant="normal">Γ</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi><mi mathvariant="normal">/</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub><mtext> (step sizes)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msubsup><mi>g</mi><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>v</mi></mrow><mn>0</mn></msubsup><mo>←</mo><mn>0</mn><mo separator="true">,</mo><mtext> </mtext><msub><mi>η</mi><mn>0</mn></msub><mo>←</mo><mtext>lr (learning rate)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>i</mi><mo>=</mo><mn>0</mn><mo separator="true">,</mo><mn>1</mn><mo separator="true">,</mo><mo>…</mo><mo separator="true">,</mo><mi>d</mi><mo>−</mo><mn>1</mn><mtext> </mtext><mrow><mi mathvariant="bold">d</mi><mi mathvariant="bold">o</mi></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><msubsup><mi>g</mi><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>v</mi></mrow><mi>i</mi></msubsup><msubsup><mi>g</mi><mi>t</mi><mi>i</mi></msubsup><mo>&gt;</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msubsup><mi>η</mi><mi>t</mi><mi>i</mi></msubsup><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi></mrow><mo stretchy="false">(</mo><msubsup><mi>η</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mi>i</mi></msubsup><msub><mi>η</mi><mo lspace="0em" rspace="0em">+</mo></msub><mo separator="true">,</mo><msub><mi mathvariant="normal">Γ</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mrow><mtext mathvariant="bold">else</mtext><mtext> </mtext><mtext mathvariant="bold">if</mtext></mrow><mtext> </mtext><msubsup><mi>g</mi><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>v</mi></mrow><mi>i</mi></msubsup><msubsup><mi>g</mi><mi>t</mi><mi>i</mi></msubsup><mo>&lt;</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msubsup><mi>η</mi><mi>t</mi><mi>i</mi></msubsup><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow><mo stretchy="false">(</mo><msubsup><mi>η</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mi>i</mi></msubsup><msub><mi>η</mi><mo lspace="0em" rspace="0em">−</mo></msub><mo separator="true">,</mo><msub><mi mathvariant="normal">Γ</mi><mrow><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msubsup><mi>g</mi><mi>t</mi><mi>i</mi></msubsup><mo>←</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">else</mtext><mtext> </mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msubsup><mi>η</mi><mi>t</mi><mi>i</mi></msubsup><mo>←</mo><msubsup><mi>η</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mi>i</mi></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><msub><mi>η</mi><mi>t</mi></msub><mrow><mi mathvariant="normal">s</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">g</mi><mi mathvariant="normal">n</mi></mrow><mo stretchy="false">(</mo><msub><mi>g</mi><mi>t</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>v</mi></mrow></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -680,7 +680,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -694,7 +694,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -718,7 +718,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -752,7 +752,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -789,7 +789,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -821,7 +821,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -853,7 +853,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -876,7 +876,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -901,7 +901,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -964,7 +964,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rprop.html#Rprop.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/rprop.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rprop.html#Rprop.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/rprop.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -976,7 +976,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.SGD.html b/main/generated/torch.optim.SGD.html
index 775c4dca734..6c27676f3e7 100644
--- a/main/generated/torch.optim.SGD.html
+++ b/main/generated/torch.optim.SGD.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.SGD">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">SGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dampening</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nesterov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sgd.html#SGD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/sgd.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">SGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dampening</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nesterov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sgd.html#SGD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/sgd.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements stochastic gradient descent (optionally with momentum).</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mtext> </mtext><mi>μ</mi><mtext> (momentum)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>τ</mi><mtext> (dampening)</mtext><mo separator="true">,</mo><mtext> </mtext><mrow><mtext> </mtext><mtext mathvariant="italic">nesterov,</mtext></mrow><mtext> </mtext><mrow><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>μ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>t</mi><mo>&gt;</mo><mn>1</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub><mo>←</mo><mi>μ</mi><msub><mtext mathvariant="bold">b</mtext><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>τ</mi><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">nesterov</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>μ</mi><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mi>γ</mi><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -724,7 +724,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -738,7 +738,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -762,7 +762,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -796,7 +796,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -833,7 +833,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -865,7 +865,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -897,7 +897,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -920,7 +920,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -945,7 +945,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1008,7 +1008,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sgd.html#SGD.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/sgd.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sgd.html#SGD.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/sgd.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1020,7 +1020,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.SparseAdam.html b/main/generated/torch.optim.SparseAdam.html
index fa6aed1e915..9df8cb3fc6e 100644
--- a/main/generated/torch.optim.SparseAdam.html
+++ b/main/generated/torch.optim.SparseAdam.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">SparseAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sparse_adam.html#SparseAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/sparse_adam.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">SparseAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sparse_adam.html#SparseAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/sparse_adam.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam" title="Permalink to this definition">¶</a></dt>
 <dd><p>SparseAdam implements a masked version of the Adam algorithm
 suitable for sparse gradients. Currently, due to implementation constraints (explained
 below), SparseAdam is only intended for a narrow subset of use cases, specifically
@@ -671,7 +671,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -685,7 +685,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -709,7 +709,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -743,7 +743,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -780,7 +780,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -812,7 +812,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -844,7 +844,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -867,7 +867,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -892,7 +892,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -955,7 +955,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sparse_adam.html#SparseAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/sparse_adam.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sparse_adam.html#SparseAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/sparse_adam.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -967,7 +967,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html b/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html
index 4392392a6eb..8d24ffadc27 100644
--- a/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html
+++ b/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ChainedScheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schedulers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ChainedScheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schedulers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Chains a list of learning rate schedulers.</p>
 <p>Takes in a sequence of chainable learning rate schedulers and calls their
 step() functions consecutively in just one call to step().</p>
@@ -642,7 +642,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -653,7 +653,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate using chainable form of the scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -664,7 +664,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -676,7 +676,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -688,7 +688,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.
@@ -697,7 +697,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.optim.lr_scheduler.ConstantLR.html b/main/generated/torch.optim.lr_scheduler.ConstantLR.html
index 7060b1c4c09..f19efd61724 100644
--- a/main/generated/torch.optim.lr_scheduler.ConstantLR.html
+++ b/main/generated/torch.optim.lr_scheduler.ConstantLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ConstantLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ConstantLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ConstantLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ConstantLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Multiply the learning rate of each parameter group by a small constant factor.</p>
 <p>The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters.
 Notice that such multiplication of the small constant factor can
@@ -652,7 +652,7 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -663,13 +663,13 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ConstantLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ConstantLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -681,7 +681,7 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -693,7 +693,7 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -701,7 +701,7 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html b/main/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html
index 06156fe60f2..f1c698568b9 100644
--- a/main/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html
+++ b/main/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CosineAnnealingLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CosineAnnealingLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the learning rate of each parameter group using a cosine annealing schedule.</p>
 <p>The <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>η</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow><annotation encoding="application/x-tex">\eta_{max}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">η</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">ma</span><span class="mord mathnormal mtight">x</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is set to the initial lr and
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>T</mi><mrow><mi>c</mi><mi>u</mi><mi>r</mi></mrow></msub></mrow><annotation encoding="application/x-tex">T_{cur}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">c</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight" style="margin-right:0.02778em;">r</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is the number of epochs since the last restart in SGDR:</p>
@@ -656,7 +656,7 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -667,13 +667,13 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Retrieve the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -685,7 +685,7 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -697,7 +697,7 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -705,7 +705,7 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html b/main/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html
index a8e41bb748e..ee492a09c76 100644
--- a/main/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html
+++ b/main/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmrestarts" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CosineAnnealingWarmRestarts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_mult</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1736"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CosineAnnealingWarmRestarts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_mult</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1736"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the learning rate of each parameter group using a cosine annealing schedule.</p>
 <p>The <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>η</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow><annotation encoding="application/x-tex">\eta_{max}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">η</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">ma</span><span class="mord mathnormal mtight">x</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is set to the initial lr, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>T</mi><mrow><mi>c</mi><mi>u</mi><mi>r</mi></mrow></msub></mrow><annotation encoding="application/x-tex">T_{cur}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">c</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight" style="margin-right:0.02778em;">r</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>
 is the number of epochs since the last restart and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>T</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">T_{i}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">i</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is the number
@@ -645,7 +645,7 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -656,13 +656,13 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the initial learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -674,7 +674,7 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -686,7 +686,7 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -694,7 +694,7 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1806"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1806"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Step could be called after every batch update.</p>
 <p class="rubric">Example</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">scheduler</span> <span class="o">=</span> <span class="n">CosineAnnealingWarmRestarts</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">T_0</span><span class="p">,</span> <span class="n">T_mult</span><span class="p">)</span>
diff --git a/main/generated/torch.optim.lr_scheduler.CyclicLR.html b/main/generated/torch.optim.lr_scheduler.CyclicLR.html
index 4f2208af157..6911bc9c8bd 100644
--- a/main/generated/torch.optim.lr_scheduler.CyclicLR.html
+++ b/main/generated/torch.optim.lr_scheduler.CyclicLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CyclicLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size_up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size_down</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'triangular'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cycle'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cycle_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CyclicLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size_up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size_down</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'triangular'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cycle'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cycle_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR).</p>
 <p>The policy cycles the learning rate between two boundaries with a constant frequency,
 as detailed in the paper <a class="reference external" href="https://arxiv.org/abs/1506.01186">Cyclical Learning Rates for Training Neural Networks</a>.
@@ -709,7 +709,7 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -720,7 +720,7 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1666"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1666"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the learning rate at batch index.</p>
 <p>This function treats <cite>self.last_epoch</cite> as the last batch index.</p>
 <p>If <cite>self.cycle_momentum</cite> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, this function has a side effect of
@@ -729,13 +729,13 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -747,7 +747,7 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.scale_fn">
-<span class="sig-name descname"><span class="pre">scale_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.scale_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1647"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.scale_fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">scale_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.scale_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1647"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.scale_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the scaling policy.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -758,7 +758,7 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.ExponentialLR.html b/main/generated/torch.optim.lr_scheduler.ExponentialLR.html
index b86d4c82d00..8bae2fee081 100644
--- a/main/generated/torch.optim.lr_scheduler.ExponentialLR.html
+++ b/main/generated/torch.optim.lr_scheduler.ExponentialLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ExponentialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ExponentialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ExponentialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ExponentialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group by gamma every epoch.</p>
 <p>When last_epoch=-1, sets initial lr as lr.</p>
 <dl class="field-list simple">
@@ -633,7 +633,7 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -644,13 +644,13 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ExponentialLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L814"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ExponentialLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L814"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -662,7 +662,7 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -674,7 +674,7 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -682,7 +682,7 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.LRScheduler.html b/main/generated/torch.optim.lr_scheduler.LRScheduler.html
index 4ac5d01c649..124bb16eea9 100644
--- a/main/generated/torch.optim.lr_scheduler.LRScheduler.html
+++ b/main/generated/torch.optim.lr_scheduler.LRScheduler.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,13 +612,13 @@
 <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LRScheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LRScheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adjusts the learning rate during optimization.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.get_last_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.get_last_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -629,7 +629,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate using chainable form of the scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -640,7 +640,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -652,7 +652,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.print_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.print_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -664,7 +664,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -672,7 +672,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.LambdaLR.html b/main/generated/torch.optim.lr_scheduler.LambdaLR.html
index 067bb1bfbac..d74bfbc20d5 100644
--- a/main/generated/torch.optim.lr_scheduler.LambdaLR.html
+++ b/main/generated/torch.optim.lr_scheduler.LambdaLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LambdaLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_lambda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LambdaLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_lambda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the initial learning rate.</p>
 <p>The learning rate of each parameter group is set to the initial lr
 times a given function. When last_epoch=-1, sets initial lr as lr.</p>
@@ -647,7 +647,7 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -658,13 +658,13 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <p>When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.</p>
 <dl class="field-list simple">
@@ -677,7 +677,7 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -689,7 +689,7 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.
@@ -700,7 +700,7 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.LinearLR.html b/main/generated/torch.optim.lr_scheduler.LinearLR.html
index 0ee0d8d1a02..ccc5bda32e6 100644
--- a/main/generated/torch.optim.lr_scheduler.LinearLR.html
+++ b/main/generated/torch.optim.lr_scheduler.LinearLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LinearLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LinearLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LinearLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LinearLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group by linearly changing small multiplicative factor.</p>
 <p>The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters.
 Notice that such decay can happen simultaneously with other changes to the learning rate
@@ -655,7 +655,7 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -666,13 +666,13 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LinearLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LinearLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -684,7 +684,7 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -696,7 +696,7 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -704,7 +704,7 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.MultiStepLR.html b/main/generated/torch.optim.lr_scheduler.MultiStepLR.html
index 45e6a862ac6..4b3a25acee1 100644
--- a/main/generated/torch.optim.lr_scheduler.MultiStepLR.html
+++ b/main/generated/torch.optim.lr_scheduler.MultiStepLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">MultiStepLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">milestones</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiStepLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">MultiStepLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">milestones</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiStepLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.</p>
 <p>Notice that such decay can happen simultaneously with other changes to the learning rate
 from outside this scheduler. When last_epoch=-1, sets initial lr as lr.</p>
@@ -648,7 +648,7 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -659,13 +659,13 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiStepLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiStepLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -677,7 +677,7 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -689,7 +689,7 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -697,7 +697,7 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.MultiplicativeLR.html b/main/generated/torch.optim.lr_scheduler.MultiplicativeLR.html
index 4d72efa5483..3fc6465fbfe 100644
--- a/main/generated/torch.optim.lr_scheduler.MultiplicativeLR.html
+++ b/main/generated/torch.optim.lr_scheduler.MultiplicativeLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">MultiplicativeLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_lambda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">MultiplicativeLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_lambda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Multiply the learning rate of each parameter group by the factor given in the specified function.</p>
 <p>When last_epoch=-1, set initial lr as lr.</p>
 <dl class="field-list simple">
@@ -644,7 +644,7 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -655,13 +655,13 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -673,7 +673,7 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -685,7 +685,7 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.
@@ -695,7 +695,7 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.OneCycleLR.html b/main/generated/torch.optim.lr_scheduler.OneCycleLR.html
index 1f8f17c9095..f61b78d5059 100644
--- a/main/generated/torch.optim.lr_scheduler.OneCycleLR.html
+++ b/main/generated/torch.optim.lr_scheduler.OneCycleLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">OneCycleLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps_per_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pct_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cycle_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.85</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">div_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_div_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">three_phase</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#OneCycleLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">OneCycleLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps_per_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pct_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cycle_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.85</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">div_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_div_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">three_phase</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#OneCycleLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the learning rate of each parameter group according to the 1cycle learning rate policy.</p>
 <p>The 1cycle policy anneals the learning rate from an initial learning rate to some maximum
 learning rate and then from that maximum learning rate to some minimum learning rate much
@@ -715,7 +715,7 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -726,13 +726,13 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#OneCycleLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L2142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#OneCycleLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L2142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -744,7 +744,7 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -756,7 +756,7 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -764,7 +764,7 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.PolynomialLR.html b/main/generated/torch.optim.lr_scheduler.PolynomialLR.html
index 4c6e93af646..2f66f1d4f53 100644
--- a/main/generated/torch.optim.lr_scheduler.PolynomialLR.html
+++ b/main/generated/torch.optim.lr_scheduler.PolynomialLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">PolynomialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#PolynomialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L976"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">PolynomialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#PolynomialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L976"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group using a polynomial function in the given total_iters.</p>
 <p>When last_epoch=-1, sets initial lr as lr.</p>
 <dl class="field-list simple">
@@ -647,7 +647,7 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -658,13 +658,13 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#PolynomialLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1019"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#PolynomialLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1019"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -676,7 +676,7 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -688,7 +688,7 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -696,7 +696,7 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html b/main/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html
index df37bd0005e..a624b596c24 100644
--- a/main/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html
+++ b/main/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ReduceLROnPlateau</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rel'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cooldown</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ReduceLROnPlateau</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rel'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cooldown</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduce learning rate when a metric has stopped improving.</p>
 <p>Models often benefit from reducing the learning rate by a factor
 of 2-10 once learning stagnates. This scheduler reads a metrics
@@ -677,7 +677,7 @@ <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Perm
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -688,7 +688,7 @@ <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate using chainable form of the scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -699,13 +699,13 @@ <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1447"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1447"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -717,7 +717,7 @@ <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metrics</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L1360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metrics</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.SequentialLR.html b/main/generated/torch.optim.lr_scheduler.SequentialLR.html
index 9f39042c528..b0d9d156663 100644
--- a/main/generated/torch.optim.lr_scheduler.SequentialLR.html
+++ b/main/generated/torch.optim.lr_scheduler.SequentialLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">SequentialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedulers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">milestones</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L826"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">SequentialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedulers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">milestones</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L826"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Contains a list of schedulers expected to be called sequentially during the optimization process.</p>
 <p>Specifically, the schedulers will be called according to the milestone points, which should provide exact
 intervals by which each scheduler should be called at a given epoch.</p>
@@ -650,7 +650,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -661,7 +661,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate using chainable form of the scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -672,7 +672,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -684,7 +684,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -696,14 +696,14 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.recursive_undo">
-<span class="sig-name descname"><span class="pre">recursive_undo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.recursive_undo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.recursive_undo" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">recursive_undo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.recursive_undo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.recursive_undo" title="Permalink to this definition">¶</a></dt>
 <dd><p>Recursively undo any step performed by the initialisation of
 schedulers.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L940"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L940"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.
@@ -712,7 +712,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.optim.lr_scheduler.StepLR.html b/main/generated/torch.optim.lr_scheduler.StepLR.html
index b992ff5871c..7b60c0dbaba 100644
--- a/main/generated/torch.optim.lr_scheduler.StepLR.html
+++ b/main/generated/torch.optim.lr_scheduler.StepLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">StepLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#StepLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">StepLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#StepLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group by gamma every step_size epochs.</p>
 <p>Notice that such decay can happen simultaneously with other changes to the learning rate
 from outside this scheduler. When last_epoch=-1, sets initial lr as lr.</p>
@@ -649,7 +649,7 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -660,13 +660,13 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#StepLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#StepLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -678,7 +678,7 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -690,7 +690,7 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -698,7 +698,7 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.swa_utils.AveragedModel.html b/main/generated/torch.optim.swa_utils.AveragedModel.html
index 27342435651..e66cb01b961 100644
--- a/main/generated/torch.optim.swa_utils.AveragedModel.html
+++ b/main/generated/torch.optim.swa_utils.AveragedModel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">AveragedModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">avg_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_avg_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/swa_utils.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">AveragedModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">avg_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_avg_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).</p>
 <p>Stochastic Weight Averaging was proposed in <a class="reference external" href="https://arxiv.org/abs/1803.05407">Averaging Weights Leads to
 Wider Optima and Better Generalization</a> by Pavel Izmailov, Dmitrii
@@ -701,7 +701,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.add_module">
-<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.add_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.add_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a child module to the current module.</p>
 <p>The module can be accessed as an attribute using the given name.</p>
 <dl class="field-list simple">
@@ -717,7 +717,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p>
 <p>Typical use includes initializing the parameters of a model
 (see also <a class="reference internal" href="../nn.init.html#nn-init-doc"><span class="std std-ref">torch.nn.init</span></a>).</p>
@@ -759,7 +759,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -777,7 +777,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.buffers">
-<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -803,7 +803,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.children">
-<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -817,7 +817,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.compile">
-<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.compile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compile this Module’s forward using <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
 <p>This Module’s <cite>__call__</cite> method is compiled and all arguments are passed as-is
 to <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
@@ -826,7 +826,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the CPU.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -844,7 +844,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the GPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -869,7 +869,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -887,7 +887,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.eval">
-<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in evaluation mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -908,7 +908,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the extra representation of the module.</p>
 <p>To print customized extra information, you should re-implement
 this method in your own modules. Both single-line and multi-line
@@ -922,7 +922,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -940,13 +940,13 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/swa_utils.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forward pass.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.get_buffer">
-<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -973,7 +973,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.get_extra_state">
-<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return any extra state to include in the module’s state_dict.</p>
 <p>Implement this and a corresponding <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.set_extra_state" title="torch.optim.swa_utils.AveragedModel.set_extra_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_extra_state()</span></code></a> for your module
 if you need to store extra state. This function is called when building the
@@ -994,7 +994,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.get_parameter">
-<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -1021,7 +1021,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.get_submodule">
-<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>For example, let’s say you have an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> <code class="docutils literal notranslate"><span class="pre">A</span></code> that
 looks like this:</p>
@@ -1070,7 +1070,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1088,7 +1088,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.ipu">
-<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.ipu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.ipu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the IPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1113,7 +1113,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy parameters and buffers from <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.state_dict" title="torch.optim.swa_utils.AveragedModel.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> into this module and its descendants.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">strict</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then
 the keys of <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.state_dict" title="torch.optim.swa_utils.AveragedModel.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> must exactly match the keys returned
@@ -1168,7 +1168,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.modules">
-<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1200,7 +1200,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.mtia">
-<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.mtia" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.mtia" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the MTIA.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1225,7 +1225,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1254,7 +1254,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.named_children">
-<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1274,7 +1274,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.named_modules">
-<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1311,7 +1311,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1341,7 +1341,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.parameters">
-<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters.</p>
 <p>This is typically passed to an optimizer.</p>
 <dl class="field-list simple">
@@ -1368,7 +1368,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_backward_hook">
-<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>This function is deprecated in favor of <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.register_full_backward_hook" title="torch.nn.Module.register_full_backward_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">register_full_backward_hook()</span></code></a> and
 the behavior of this function will change in future versions.</p>
@@ -1385,7 +1385,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_buffer">
-<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a buffer to the module.</p>
 <p>This is typically used to register a buffer that should not to be
 considered a model parameter. For example, BatchNorm’s <code class="docutils literal notranslate"><span class="pre">running_mean</span></code>
@@ -1417,7 +1417,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_forward_hook">
-<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_forward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_forward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward hook on the module.</p>
 <p>The hook will be called every time after <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.forward" title="torch.optim.swa_utils.AveragedModel.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> has computed an output.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> or not specified, the input contains only
@@ -1468,7 +1468,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_forward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward pre-hook on the module.</p>
 <p>The hook will be called every time before <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.forward" title="torch.optim.swa_utils.AveragedModel.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> is invoked.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is false or not specified, the input contains only
@@ -1518,7 +1518,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_full_backward_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>The hook will be called every time the gradients with respect to a module
 are computed, i.e. the hook will execute if and only if the gradients with
@@ -1569,7 +1569,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_full_backward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook on the module.</p>
 <p>The hook will be called every time the gradients for the module are computed.
 The hook should have the following signature:</p>
@@ -1615,7 +1615,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook to be run after module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, incompatible_keys) -&gt; None</p>
@@ -1645,7 +1645,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook to be run before module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -&gt; None  # noqa: B950</p>
@@ -1661,7 +1661,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_module">
-<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Alias for <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.add_module" title="torch.optim.swa_utils.AveragedModel.add_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
@@ -1669,7 +1669,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_parameter">
-<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a parameter to the module.</p>
 <p>The parameter can be accessed as an attribute using given name.</p>
 <dl class="field-list simple">
@@ -1688,7 +1688,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook for the <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata) -&gt; None</p>
@@ -1699,7 +1699,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook for the <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, prefix, keep_vars) -&gt; None</p>
@@ -1711,7 +1711,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.requires_grad_">
-<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.requires_grad_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.requires_grad_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Change if autograd should record operations on parameters in this module.</p>
 <p>This method sets the parameters’ <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad</span></code> attributes
 in-place.</p>
@@ -1735,7 +1735,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.set_extra_state">
-<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.set_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.set_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p>
 <p>This function is called from <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.load_state_dict" title="torch.optim.swa_utils.AveragedModel.load_state_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> to handle any extra state
 found within the <cite>state_dict</cite>. Implement this function and a corresponding
@@ -1750,7 +1750,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.set_submodule">
-<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.set_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.set_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1808,7 +1808,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.share_memory">
-<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.share_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.share_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.Tensor.share_memory_.html#torch.Tensor.share_memory_" title="torch.Tensor.share_memory_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1819,7 +1819,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary containing references to the whole state of the module.</p>
 <p>Both parameters and persistent buffers (e.g. running averages) are
 included. Keys are corresponding parameter and buffer names.
@@ -1872,27 +1872,27 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move and/or cast the parameters and buffers.</p>
 <p>This can be called as</p>
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <p>Its signature is similar to <a class="reference internal" href="torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.to()</span></code></a>, but only accepts
@@ -1971,7 +1971,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.to_empty">
-<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.to_empty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.to_empty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move the parameters and buffers to the specified device without copying storage.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1993,7 +1993,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.train">
-<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.train" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.train" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in training mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -2015,7 +2015,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2036,7 +2036,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.update_parameters">
-<span class="sig-name descname"><span class="pre">update_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel.update_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/swa_utils.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.update_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel.update_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.update_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update model parameters.</p>
 <dl class="field-list simple">
 </dl>
@@ -2044,7 +2044,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.xpu">
-<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.xpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.xpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the XPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing optimizer if the module will
@@ -2069,7 +2069,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset gradients of all model parameters.</p>
 <p>See similar function under <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> for more context.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.optim.swa_utils.SWALR.html b/main/generated/torch.optim.swa_utils.SWALR.html
index 782ff9c273e..a1004d81d3e 100644
--- a/main/generated/torch.optim.swa_utils.SWALR.html
+++ b/main/generated/torch.optim.swa_utils.SWALR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">SWALR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swa_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#SWALR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/swa_utils.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">SWALR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swa_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#SWALR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Anneals the learning rate in each parameter group to a fixed value.</p>
 <p>This learning rate scheduler is meant to be used with Stochastic Weight
 Averaging (SWA) method (see <cite>torch.optim.swa_utils.AveragedModel</cite>).</p>
@@ -655,7 +655,7 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -666,13 +666,13 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#SWALR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/swa_utils.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#SWALR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -684,7 +684,7 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -696,7 +696,7 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -704,7 +704,7 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.orgqr.html b/main/generated/torch.orgqr.html
index 6e3362be238..4c7a04bdb01 100644
--- a/main/generated/torch.orgqr.html
+++ b/main/generated/torch.orgqr.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ormqr.html b/main/generated/torch.ormqr.html
index 97346c8d6ba..dfe9124f3a1 100644
--- a/main/generated/torch.ormqr.html
+++ b/main/generated/torch.ormqr.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.outer.html b/main/generated/torch.outer.html
index 5594f6e7adf..9bea851afb3 100644
--- a/main/generated/torch.outer.html
+++ b/main/generated/torch.outer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.pca_lowrank.html b/main/generated/torch.pca_lowrank.html
index 49af4e66704..0333a7f9984 100644
--- a/main/generated/torch.pca_lowrank.html
+++ b/main/generated/torch.pca_lowrank.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.pca_lowrank<a class="headerlink" href="#torch-pca-lowrank" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.pca_lowrank">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">pca_lowrank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lowrank.html#pca_lowrank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_lowrank.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.pca_lowrank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">pca_lowrank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lowrank.html#pca_lowrank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_lowrank.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.pca_lowrank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs linear Principal Component Analysis (PCA) on a low-rank
 matrix, batches of such matrices, or sparse matrix.</p>
 <p>This function returns a namedtuple <code class="docutils literal notranslate"><span class="pre">(U,</span> <span class="pre">S,</span> <span class="pre">V)</span></code> which is the
diff --git a/main/generated/torch.permute.html b/main/generated/torch.permute.html
index a4dc3492a36..a885e97dd19 100644
--- a/main/generated/torch.permute.html
+++ b/main/generated/torch.permute.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.pinverse.html b/main/generated/torch.pinverse.html
index 7dc16d7be28..765acdd4941 100644
--- a/main/generated/torch.pinverse.html
+++ b/main/generated/torch.pinverse.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.poisson.html b/main/generated/torch.poisson.html
index fcdb5234576..66279d5d2f9 100644
--- a/main/generated/torch.poisson.html
+++ b/main/generated/torch.poisson.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.polar.html b/main/generated/torch.polar.html
index ba538f43e25..8080972b568 100644
--- a/main/generated/torch.polar.html
+++ b/main/generated/torch.polar.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.polygamma.html b/main/generated/torch.polygamma.html
index 376cf446cad..92710f506e6 100644
--- a/main/generated/torch.polygamma.html
+++ b/main/generated/torch.polygamma.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.positive.html b/main/generated/torch.positive.html
index 8ae5f90fe7b..7a50354d43e 100644
--- a/main/generated/torch.positive.html
+++ b/main/generated/torch.positive.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.pow.html b/main/generated/torch.pow.html
index 1014f112319..77e599a0d81 100644
--- a/main/generated/torch.pow.html
+++ b/main/generated/torch.pow.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.prod.html b/main/generated/torch.prod.html
index d49c8d4f65b..5ab3c4b953a 100644
--- a/main/generated/torch.prod.html
+++ b/main/generated/torch.prod.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.promote_types.html b/main/generated/torch.promote_types.html
index 4ba9f787598..bc00e42a967 100644
--- a/main/generated/torch.promote_types.html
+++ b/main/generated/torch.promote_types.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.qr.html b/main/generated/torch.qr.html
index bf26b6b1051..e9bb1d553a0 100644
--- a/main/generated/torch.qr.html
+++ b/main/generated/torch.qr.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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diff --git a/main/generated/torch.quantile.html b/main/generated/torch.quantile.html
index ceb152e59cb..0612f686d78 100644
--- a/main/generated/torch.quantile.html
+++ b/main/generated/torch.quantile.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quantize_per_channel.html b/main/generated/torch.quantize_per_channel.html
index 8a3c0c35b4d..6bc93e1670c 100644
--- a/main/generated/torch.quantize_per_channel.html
+++ b/main/generated/torch.quantize_per_channel.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quantize_per_tensor.html b/main/generated/torch.quantize_per_tensor.html
index cdace5bb45e..7729c5e586c 100644
--- a/main/generated/torch.quantize_per_tensor.html
+++ b/main/generated/torch.quantize_per_tensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quantized_batch_norm.html b/main/generated/torch.quantized_batch_norm.html
index ba23c1b8762..03901f30abc 100644
--- a/main/generated/torch.quantized_batch_norm.html
+++ b/main/generated/torch.quantized_batch_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quantized_max_pool1d.html b/main/generated/torch.quantized_max_pool1d.html
index ea2abb0fe5b..008d1cede77 100644
--- a/main/generated/torch.quantized_max_pool1d.html
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@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quantized_max_pool2d.html b/main/generated/torch.quantized_max_pool2d.html
index 932b798964e..10d9061597b 100644
--- a/main/generated/torch.quantized_max_pool2d.html
+++ b/main/generated/torch.quantized_max_pool2d.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quasirandom.SobolEngine.html b/main/generated/torch.quasirandom.SobolEngine.html
index 88f2b3af45a..c36e3f5818c 100644
--- a/main/generated/torch.quasirandom.SobolEngine.html
+++ b/main/generated/torch.quasirandom.SobolEngine.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.quasirandom.</span></span><span class="sig-name descname"><span class="pre">SobolEngine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimension</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scramble</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/quasirandom.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.quasirandom.</span></span><span class="sig-name descname"><span class="pre">SobolEngine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimension</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scramble</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine" title="Permalink to this definition">¶</a></dt>
 <dd><p>The <a class="reference internal" href="#torch.quasirandom.SobolEngine" title="torch.quasirandom.SobolEngine"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.quasirandom.SobolEngine</span></code></a> is an engine for generating
 (scrambled) Sobol sequences. Sobol sequences are an example of low
 discrepancy quasi-random sequences.</p>
@@ -654,7 +654,7 @@ <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to thi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine.draw">
-<span class="sig-name descname"><span class="pre">draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.draw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/quasirandom.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.draw" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.draw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.draw" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to draw a sequence of <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> points from a Sobol sequence.
 Note that the samples are dependent on the previous samples. The size
 of the result is <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>n</mi><mo separator="true">,</mo><mi>d</mi><mi>i</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(n, dimension)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal">n</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">d</span><span class="mord mathnormal">im</span><span class="mord mathnormal">e</span><span class="mord mathnormal">n</span><span class="mord mathnormal">s</span><span class="mord mathnormal">i</span><span class="mord mathnormal">o</span><span class="mord mathnormal">n</span><span class="mclose">)</span></span></span></span></span>.</p>
@@ -677,7 +677,7 @@ <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine.draw_base2">
-<span class="sig-name descname"><span class="pre">draw_base2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.draw_base2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/quasirandom.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.draw_base2" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">draw_base2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.draw_base2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.draw_base2" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to draw a sequence of <code class="xref py py-attr docutils literal notranslate"><span class="pre">2**m</span></code> points from a Sobol sequence.
 Note that the samples are dependent on the previous samples. The size
 of the result is <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mn>2</mn><mo>∗</mo><mo>∗</mo><mi>m</mi><mo separator="true">,</mo><mi>d</mi><mi>i</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(2**m, dimension)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">2</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">∗</span><span class="mord mathnormal">m</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">d</span><span class="mord mathnormal">im</span><span class="mord mathnormal">e</span><span class="mord mathnormal">n</span><span class="mord mathnormal">s</span><span class="mord mathnormal">i</span><span class="mord mathnormal">o</span><span class="mord mathnormal">n</span><span class="mclose">)</span></span></span></span></span>.</p>
@@ -699,7 +699,7 @@ <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine.fast_forward">
-<span class="sig-name descname"><span class="pre">fast_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.fast_forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/quasirandom.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.fast_forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fast_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.fast_forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.fast_forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to fast-forward the state of the <code class="docutils literal notranslate"><span class="pre">SobolEngine</span></code> by
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> steps. This is equivalent to drawing <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> samples
 without using the samples.</p>
@@ -712,7 +712,7 @@ <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine.reset">
-<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/quasirandom.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to reset the <code class="docutils literal notranslate"><span class="pre">SobolEngine</span></code> to base state.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.rad2deg.html b/main/generated/torch.rad2deg.html
index 0181fe6bf14..e8fb1593d92 100644
--- a/main/generated/torch.rad2deg.html
+++ b/main/generated/torch.rad2deg.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.rand.html b/main/generated/torch.rand.html
index 5cc46a83520..d6764af9104 100644
--- a/main/generated/torch.rand.html
+++ b/main/generated/torch.rand.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.rand_like.html b/main/generated/torch.rand_like.html
index cedd3d34961..006637cbb53 100644
--- a/main/generated/torch.rand_like.html
+++ b/main/generated/torch.rand_like.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.randint.html b/main/generated/torch.randint.html
index e7c44ec9f80..d3cc6a47248 100644
--- a/main/generated/torch.randint.html
+++ b/main/generated/torch.randint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.randint_like.html b/main/generated/torch.randint_like.html
index a4ed1c821da..e16aa51c161 100644
--- a/main/generated/torch.randint_like.html
+++ b/main/generated/torch.randint_like.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
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index 16bddba0f99..15f8db79d6c 100644
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diff --git a/main/generated/torch.ravel.html b/main/generated/torch.ravel.html
index 81c23b44dec..a650902f2c0 100644
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diff --git a/main/generated/torch.real.html b/main/generated/torch.real.html
index 431333ab403..0c0588bec00 100644
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+++ b/main/generated/torch.real.html
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diff --git a/main/generated/torch.reciprocal.html b/main/generated/torch.reciprocal.html
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index 44ee044cf2f..60401497dc0 100644
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diff --git a/main/generated/torch.repeat_interleave.html b/main/generated/torch.repeat_interleave.html
index bebad4264e7..3873962e277 100644
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diff --git a/main/generated/torch.reshape.html b/main/generated/torch.reshape.html
index 391b00cd061..29c5743e2b2 100644
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diff --git a/main/generated/torch.resolve_conj.html b/main/generated/torch.resolve_conj.html
index 1a66e748463..f9ce365abc3 100644
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diff --git a/main/generated/torch.resolve_neg.html b/main/generated/torch.resolve_neg.html
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diff --git a/main/generated/torch.result_type.html b/main/generated/torch.result_type.html
index 5fbc1ed08d0..9aa59267a57 100644
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diff --git a/main/generated/torch.roll.html b/main/generated/torch.roll.html
index a8527959159..345a832dc4a 100644
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diff --git a/main/generated/torch.rot90.html b/main/generated/torch.rot90.html
index 374813f2c84..8c0e0424e23 100644
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diff --git a/main/generated/torch.round.html b/main/generated/torch.round.html
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diff --git a/main/generated/torch.row_stack.html b/main/generated/torch.row_stack.html
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diff --git a/main/generated/torch.rsqrt.html b/main/generated/torch.rsqrt.html
index bea4e8641a5..cf9355b48ac 100644
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diff --git a/main/generated/torch.save.html b/main/generated/torch.save.html
index 6ee495683f0..6e53735e902 100644
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@@ -612,7 +612,7 @@
 <h1>torch.save<a class="headerlink" href="#torch-save" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.save">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">pickle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_new_zipfile_serialization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">pickle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_new_zipfile_serialization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Saves an object to a disk file.</p>
 <p>See also: <a class="reference internal" href="../notes/serialization.html#saving-loading-tensors"><span class="std std-ref">Saving and loading tensors</span></a></p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.scatter.html b/main/generated/torch.scatter.html
index ea5d735f9c1..836aed89c97 100644
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diff --git a/main/generated/torch.scatter_add.html b/main/generated/torch.scatter_add.html
index e5601431cb0..d5167d85517 100644
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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diff --git a/main/generated/torch.scatter_reduce.html b/main/generated/torch.scatter_reduce.html
index c97a805ece1..d9ff855c0b1 100644
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index 8efdf7e21f9..9a042556606 100644
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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diff --git a/main/generated/torch.seed.html b/main/generated/torch.seed.html
index 2d1e8602d1e..11374984fc1 100644
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>torch.seed<a class="headerlink" href="#torch-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers to a non-deterministic
 random number on all devices. Returns a 64 bit number used to seed the RNG.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.select.html b/main/generated/torch.select.html
index d3c668587be..22efee91bc9 100644
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@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.select_scatter.html b/main/generated/torch.select_scatter.html
index fee1deafdce..288aee31773 100644
--- a/main/generated/torch.select_scatter.html
+++ b/main/generated/torch.select_scatter.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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     <div id="searchBox">
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diff --git a/main/generated/torch.set_default_device.html b/main/generated/torch.set_default_device.html
index 6b4d253e2f1..6bd0fd57627 100644
--- a/main/generated/torch.set_default_device.html
+++ b/main/generated/torch.set_default_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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     </div>
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@@ -612,7 +612,7 @@
 <h1>torch.set_default_device<a class="headerlink" href="#torch-set-default-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_default_device">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the default <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> to be allocated on <code class="docutils literal notranslate"><span class="pre">device</span></code>.  This
 does not affect factory function calls which are called with an explicit
 <code class="docutils literal notranslate"><span class="pre">device</span></code> argument.  Factory calls will be performed as if they
diff --git a/main/generated/torch.set_default_dtype.html b/main/generated/torch.set_default_dtype.html
index 1eb639a93bf..fbbbbe31c46 100644
--- a/main/generated/torch.set_default_dtype.html
+++ b/main/generated/torch.set_default_dtype.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.set_default_dtype<a class="headerlink" href="#torch-set-default-dtype" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_default_dtype">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_dtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_dtype" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_dtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_dtype" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the default floating point dtype to <code class="xref py py-attr docutils literal notranslate"><span class="pre">d</span></code>. Supports floating point dtype
 as inputs. Other dtypes will cause torch to raise an exception.</p>
 <p>When PyTorch is initialized its default floating point dtype is torch.float32,
diff --git a/main/generated/torch.set_default_tensor_type.html b/main/generated/torch.set_default_tensor_type.html
index ebf4d2aa674..9cc309e4702 100644
--- a/main/generated/torch.set_default_tensor_type.html
+++ b/main/generated/torch.set_default_tensor_type.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.set_default_tensor_type<a class="headerlink" href="#torch-set-default-tensor-type" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_default_tensor_type">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_tensor_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_tensor_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_tensor_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_tensor_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_tensor_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_tensor_type" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This function is deprecated as of PyTorch 2.1, please use <a class="reference internal" href="torch.set_default_dtype.html#torch.set_default_dtype" title="torch.set_default_dtype"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_dtype()</span></code></a> and
diff --git a/main/generated/torch.set_deterministic_debug_mode.html b/main/generated/torch.set_deterministic_debug_mode.html
index 2842e77c03a..653ad5b61a3 100644
--- a/main/generated/torch.set_deterministic_debug_mode.html
+++ b/main/generated/torch.set_deterministic_debug_mode.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.set_deterministic_debug_mode<a class="headerlink" href="#torch-set-deterministic-debug-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_deterministic_debug_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_deterministic_debug_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_deterministic_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_deterministic_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_deterministic_debug_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_deterministic_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_deterministic_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the debug mode for deterministic operations.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.set_float32_matmul_precision.html b/main/generated/torch.set_float32_matmul_precision.html
index b47b67bb664..9d962318fff 100644
--- a/main/generated/torch.set_float32_matmul_precision.html
+++ b/main/generated/torch.set_float32_matmul_precision.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.set_float32_matmul_precision<a class="headerlink" href="#torch-set-float32-matmul-precision" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_float32_matmul_precision">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_float32_matmul_precision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_float32_matmul_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_float32_matmul_precision" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_float32_matmul_precision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_float32_matmul_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_float32_matmul_precision" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the internal precision of float32 matrix multiplications.</p>
 <p>Running float32 matrix multiplications in lower precision may significantly increase
 performance, and in some programs the loss of precision has a negligible impact.</p>
diff --git a/main/generated/torch.set_flush_denormal.html b/main/generated/torch.set_flush_denormal.html
index e434678123b..22c1fe81990 100644
--- a/main/generated/torch.set_flush_denormal.html
+++ b/main/generated/torch.set_flush_denormal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.set_num_interop_threads.html b/main/generated/torch.set_num_interop_threads.html
index c2fd56ced74..eb3c458bcb7 100644
--- a/main/generated/torch.set_num_interop_threads.html
+++ b/main/generated/torch.set_num_interop_threads.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.set_num_threads.html b/main/generated/torch.set_num_threads.html
index 4f01b14afdd..00d4e04b725 100644
--- a/main/generated/torch.set_num_threads.html
+++ b/main/generated/torch.set_num_threads.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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diff --git a/main/generated/torch.set_printoptions.html b/main/generated/torch.set_printoptions.html
index 509e5c0648c..18f0265a66e 100644
--- a/main/generated/torch.set_printoptions.html
+++ b/main/generated/torch.set_printoptions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.set_printoptions<a class="headerlink" href="#torch-set-printoptions" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_printoptions">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_printoptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edgeitems</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sci_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor_str.html#set_printoptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor_str.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_printoptions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_printoptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edgeitems</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sci_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor_str.html#set_printoptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor_str.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_printoptions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set options for printing. Items shamelessly taken from NumPy</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.set_rng_state.html b/main/generated/torch.set_rng_state.html
index 437ff253203..55372ab0efc 100644
--- a/main/generated/torch.set_rng_state.html
+++ b/main/generated/torch.set_rng_state.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.set_rng_state<a class="headerlink" href="#torch-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the random number generator state.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.set_warn_always.html b/main/generated/torch.set_warn_always.html
index 76db6a0849a..3457c9b19d1 100644
--- a/main/generated/torch.set_warn_always.html
+++ b/main/generated/torch.set_warn_always.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.set_warn_always<a class="headerlink" href="#torch-set-warn-always" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_warn_always">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_warn_always</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_warn_always"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_warn_always" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_warn_always</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_warn_always"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_warn_always" title="Permalink to this definition">¶</a></dt>
 <dd><p>When this flag is False (default) then some PyTorch warnings may only
 appear once per process. This helps avoid excessive warning information.
 Setting it to True causes these warnings to always appear, which may be
diff --git a/main/generated/torch.sgn.html b/main/generated/torch.sgn.html
index fb46c9b1970..2a8d34a769c 100644
--- a/main/generated/torch.sgn.html
+++ b/main/generated/torch.sgn.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sigmoid.html b/main/generated/torch.sigmoid.html
index ad164b7c33e..b8828f93fe4 100644
--- a/main/generated/torch.sigmoid.html
+++ b/main/generated/torch.sigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sign.html b/main/generated/torch.sign.html
index abc75785dc0..a15ac2b4372 100644
--- a/main/generated/torch.sign.html
+++ b/main/generated/torch.sign.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.signal.windows.bartlett.html b/main/generated/torch.signal.windows.bartlett.html
index 1cd0344ff5e..17e54071bd4 100644
--- a/main/generated/torch.signal.windows.bartlett.html
+++ b/main/generated/torch.signal.windows.bartlett.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.bartlett<a class="headerlink" href="#torch-signal-windows-bartlett" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.bartlett">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">bartlett</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#bartlett"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L596"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.bartlett" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">bartlett</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#bartlett"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L596"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.bartlett" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Bartlett window.</p>
 <p>The Bartlett window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.blackman.html b/main/generated/torch.signal.windows.blackman.html
index 2b1bad2da83..4e9b861a510 100644
--- a/main/generated/torch.signal.windows.blackman.html
+++ b/main/generated/torch.signal.windows.blackman.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.blackman<a class="headerlink" href="#torch-signal-windows-blackman" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.blackman">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">blackman</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#blackman"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.blackman" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">blackman</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#blackman"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.blackman" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Blackman window.</p>
 <p>The Blackman window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.cosine.html b/main/generated/torch.signal.windows.cosine.html
index 4a86ca5438f..094ca77fdd7 100644
--- a/main/generated/torch.signal.windows.cosine.html
+++ b/main/generated/torch.signal.windows.cosine.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.cosine<a class="headerlink" href="#torch-signal-windows-cosine" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.cosine">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">cosine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#cosine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.cosine" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">cosine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#cosine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.cosine" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a window with a simple cosine waveform, following the same implementation as SciPy.
 This window is also known as the sine window.</p>
 <p>The cosine window is defined as follows:</p>
diff --git a/main/generated/torch.signal.windows.exponential.html b/main/generated/torch.signal.windows.exponential.html
index 754279dc33a..cdf85aff2b3 100644
--- a/main/generated/torch.signal.windows.exponential.html
+++ b/main/generated/torch.signal.windows.exponential.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.exponential<a class="headerlink" href="#torch-signal-windows-exponential" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.exponential">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">exponential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#exponential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.exponential" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">exponential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#exponential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.exponential" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a window with an exponential waveform.
 Also known as Poisson window.</p>
 <p>The exponential window is defined as follows:</p>
diff --git a/main/generated/torch.signal.windows.gaussian.html b/main/generated/torch.signal.windows.gaussian.html
index bc4600d71a7..0e1ad64fcdb 100644
--- a/main/generated/torch.signal.windows.gaussian.html
+++ b/main/generated/torch.signal.windows.gaussian.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.gaussian<a class="headerlink" href="#torch-signal-windows-gaussian" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.gaussian">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L256"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.gaussian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L256"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.gaussian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a window with a gaussian waveform.</p>
 <p>The gaussian window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.general_cosine.html b/main/generated/torch.signal.windows.general_cosine.html
index 66dab300309..f598e7f7ba9 100644
--- a/main/generated/torch.signal.windows.general_cosine.html
+++ b/main/generated/torch.signal.windows.general_cosine.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.general_cosine<a class="headerlink" href="#torch-signal-windows-general-cosine" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.general_cosine">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">general_cosine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#general_cosine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.general_cosine" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">general_cosine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#general_cosine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.general_cosine" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the general cosine window.</p>
 <p>The general cosine window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.general_hamming.html b/main/generated/torch.signal.windows.general_hamming.html
index 55b396b622e..d235572ea76 100644
--- a/main/generated/torch.signal.windows.general_hamming.html
+++ b/main/generated/torch.signal.windows.general_hamming.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.general_hamming<a class="headerlink" href="#torch-signal-windows-general-hamming" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.general_hamming">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">general_hamming</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.54</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#general_hamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L769"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.general_hamming" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">general_hamming</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.54</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#general_hamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L769"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.general_hamming" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the general Hamming window.</p>
 <p>The general Hamming window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.hamming.html b/main/generated/torch.signal.windows.hamming.html
index 0146a7c85f6..f7ef2a49d1f 100644
--- a/main/generated/torch.signal.windows.hamming.html
+++ b/main/generated/torch.signal.windows.hamming.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.hamming<a class="headerlink" href="#torch-signal-windows-hamming" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.hamming">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">hamming</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#hamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.hamming" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">hamming</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#hamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.hamming" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Hamming window.</p>
 <p>The Hamming window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.hann.html b/main/generated/torch.signal.windows.hann.html
index 2740010a53c..c040d627f18 100644
--- a/main/generated/torch.signal.windows.hann.html
+++ b/main/generated/torch.signal.windows.hann.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.hann<a class="headerlink" href="#torch-signal-windows-hann" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.hann">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">hann</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#hann"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.hann" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">hann</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#hann"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.hann" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Hann window.</p>
 <p>The Hann window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.kaiser.html b/main/generated/torch.signal.windows.kaiser.html
index 5d602cb712e..94851f4a4a3 100644
--- a/main/generated/torch.signal.windows.kaiser.html
+++ b/main/generated/torch.signal.windows.kaiser.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.kaiser<a class="headerlink" href="#torch-signal-windows-kaiser" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.kaiser">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">kaiser</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#kaiser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L334"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.kaiser" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">kaiser</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#kaiser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L334"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.kaiser" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Kaiser window.</p>
 <p>The Kaiser window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.nuttall.html b/main/generated/torch.signal.windows.nuttall.html
index b8d95923c16..5fdc52d95d6 100644
--- a/main/generated/torch.signal.windows.nuttall.html
+++ b/main/generated/torch.signal.windows.nuttall.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.nuttall<a class="headerlink" href="#torch-signal-windows-nuttall" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.nuttall">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">nuttall</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#nuttall"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/signal/windows/windows.py#L827"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.nuttall" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">nuttall</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#nuttall"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L827"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.nuttall" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the minimum 4-term Blackman-Harris window according to Nuttall.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>w</mi><mi>n</mi></msub><mo>=</mo><mn>1</mn><mo>−</mo><mn>0.36358</mn><mi>cos</mi><mo>⁡</mo><mrow><mo stretchy="false">(</mo><msub><mi>z</mi><mi>n</mi></msub><mo stretchy="false">)</mo></mrow><mo>+</mo><mn>0.48917</mn><mi>cos</mi><mo>⁡</mo><mrow><mo stretchy="false">(</mo><mn>2</mn><msub><mi>z</mi><mi>n</mi></msub><mo stretchy="false">)</mo></mrow><mo>−</mo><mn>0.13659</mn><mi>cos</mi><mo>⁡</mo><mrow><mo stretchy="false">(</mo><mn>3</mn><msub><mi>z</mi><mi>n</mi></msub><mo stretchy="false">)</mo></mrow><mo>+</mo><mn>0.01064</mn><mi>cos</mi><mo>⁡</mo><mrow><mo stretchy="false">(</mo><mn>4</mn><msub><mi>z</mi><mi>n</mi></msub><mo stretchy="false">)</mo></mrow></mrow><annotation encoding="application/x-tex">w_n = 1 - 0.36358 \cos{(z_n)} + 0.48917 \cos{(2z_n)} - 0.13659 \cos{(3z_n)} + 0.01064 \cos{(4z_n)}
diff --git a/main/generated/torch.signbit.html b/main/generated/torch.signbit.html
index 318789b5a08..9424436007e 100644
--- a/main/generated/torch.signbit.html
+++ b/main/generated/torch.signbit.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sin.html b/main/generated/torch.sin.html
index 75534c1b635..229ea4490b6 100644
--- a/main/generated/torch.sin.html
+++ b/main/generated/torch.sin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sinc.html b/main/generated/torch.sinc.html
index 18581bfee0c..994a9a49974 100644
--- a/main/generated/torch.sinc.html
+++ b/main/generated/torch.sinc.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sinh.html b/main/generated/torch.sinh.html
index 6d74bff6223..5392de309d9 100644
--- a/main/generated/torch.sinh.html
+++ b/main/generated/torch.sinh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.slice_scatter.html b/main/generated/torch.slice_scatter.html
index 0e9503dc862..74f6b3cffd7 100644
--- a/main/generated/torch.slice_scatter.html
+++ b/main/generated/torch.slice_scatter.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.slogdet.html b/main/generated/torch.slogdet.html
index 0df3318cd08..76595a26639 100644
--- a/main/generated/torch.slogdet.html
+++ b/main/generated/torch.slogdet.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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diff --git a/main/generated/torch.smm.html b/main/generated/torch.smm.html
index ab86eafa90a..b2672fa7f64 100644
--- a/main/generated/torch.smm.html
+++ b/main/generated/torch.smm.html
@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/generated/torch.softmax.html b/main/generated/torch.softmax.html
index 79633cf0fce..04cd197ef1c 100644
--- a/main/generated/torch.softmax.html
+++ b/main/generated/torch.softmax.html
@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/generated/torch.sort.html b/main/generated/torch.sort.html
index b455a59131a..94f7a1d7537 100644
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/generated/torch.sparse.addmm.html b/main/generated/torch.sparse.addmm.html
index c9ff4202ad2..49f21f0ad5e 100644
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diff --git a/main/generated/torch.sparse.as_sparse_gradcheck.html b/main/generated/torch.sparse.as_sparse_gradcheck.html
index 4d6e718ef07..a6fb2d0b9be 100644
--- a/main/generated/torch.sparse.as_sparse_gradcheck.html
+++ b/main/generated/torch.sparse.as_sparse_gradcheck.html
@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>torch.sparse.as_sparse_gradcheck<a class="headerlink" href="#torch-sparse-as-sparse-gradcheck" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sparse.as_sparse_gradcheck">
-<span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">as_sparse_gradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradcheck</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#as_sparse_gradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/sparse/__init__.py#L549"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.as_sparse_gradcheck" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">as_sparse_gradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradcheck</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#as_sparse_gradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L549"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.as_sparse_gradcheck" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decorate function, to extend gradcheck for sparse tensors.</p>
 <p>Decorator for torch.autograd.gradcheck or its functools.partial
 variants that extends the gradcheck function with support to input
diff --git a/main/generated/torch.sparse.check_sparse_tensor_invariants.html b/main/generated/torch.sparse.check_sparse_tensor_invariants.html
index bdfdbda88d8..d9929813cce 100644
--- a/main/generated/torch.sparse.check_sparse_tensor_invariants.html
+++ b/main/generated/torch.sparse.check_sparse_tensor_invariants.html
@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>check_sparse_tensor_invariants<a class="headerlink" href="#check-sparse-tensor-invariants" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.sparse.check_sparse_tensor_invariants">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">check_sparse_tensor_invariants</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/sparse/__init__.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">check_sparse_tensor_invariants</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants" title="Permalink to this definition">¶</a></dt>
 <dd><p>A tool to control checking sparse tensor invariants.</p>
 <p>The following options exists to manage sparsr tensor invariants
 checking in sparse tensor construction:</p>
@@ -655,14 +655,14 @@ <h1>check_sparse_tensor_invariants<a class="headerlink" href="#check-sparse-tens
 </ol>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.sparse.check_sparse_tensor_invariants.disable">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">disable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.disable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/sparse/__init__.py#L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.disable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">disable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.disable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.disable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable sparse tensor invariants checking in sparse tensor constructors.</p>
 <p>See <a class="reference internal" href="#torch.sparse.check_sparse_tensor_invariants.enable" title="torch.sparse.check_sparse_tensor_invariants.enable"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sparse.check_sparse_tensor_invariants.enable()</span></code></a> for more information.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.sparse.check_sparse_tensor_invariants.enable">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">enable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/sparse/__init__.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.enable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">enable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable sparse tensor invariants checking in sparse tensor constructors.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -681,7 +681,7 @@ <h1>check_sparse_tensor_invariants<a class="headerlink" href="#check-sparse-tens
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.sparse.check_sparse_tensor_invariants.is_enabled">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/sparse/__init__.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.is_enabled" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.is_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if the sparse tensor invariants checking is enabled.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.sparse.log_softmax.html b/main/generated/torch.sparse.log_softmax.html
index 4103bc94d32..0117a05e347 100644
--- a/main/generated/torch.sparse.log_softmax.html
+++ b/main/generated/torch.sparse.log_softmax.html
@@ -300,7 +300,7 @@
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diff --git a/main/generated/torch.sparse.mm.html b/main/generated/torch.sparse.mm.html
index a1a8bf00e54..09390c3ec7b 100644
--- a/main/generated/torch.sparse.mm.html
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@@ -300,7 +300,7 @@
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     <div class="version">
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diff --git a/main/generated/torch.sparse.sampled_addmm.html b/main/generated/torch.sparse.sampled_addmm.html
index 9d6ff15c00d..cce71034f1d 100644
--- a/main/generated/torch.sparse.sampled_addmm.html
+++ b/main/generated/torch.sparse.sampled_addmm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sparse.softmax.html b/main/generated/torch.sparse.softmax.html
index e4c824b3b68..b48fb0bc70e 100644
--- a/main/generated/torch.sparse.softmax.html
+++ b/main/generated/torch.sparse.softmax.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sparse.spdiags.html b/main/generated/torch.sparse.spdiags.html
index f2c5111c33a..cc2adf8dc0a 100644
--- a/main/generated/torch.sparse.spdiags.html
+++ b/main/generated/torch.sparse.spdiags.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sparse.spsolve.html b/main/generated/torch.sparse.spsolve.html
index a50fd1f6e8b..8d091c7ec37 100644
--- a/main/generated/torch.sparse.spsolve.html
+++ b/main/generated/torch.sparse.spsolve.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sparse.sum.html b/main/generated/torch.sparse.sum.html
index 330392892db..14949839018 100644
--- a/main/generated/torch.sparse.sum.html
+++ b/main/generated/torch.sparse.sum.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sparse.sum<a class="headerlink" href="#torch-sparse-sum" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sparse.sum">
-<span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/sparse/__init__.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.sum" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.sum" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the sum of each row of the given sparse tensor.</p>
 <p>Returns the sum of each row of the sparse tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> in the given
 dimensions <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is a list of dimensions,
diff --git a/main/generated/torch.sparse_bsc_tensor.html b/main/generated/torch.sparse_bsc_tensor.html
index 705bd4afdbd..3cf96bdd344 100644
--- a/main/generated/torch.sparse_bsc_tensor.html
+++ b/main/generated/torch.sparse_bsc_tensor.html
@@ -300,7 +300,7 @@
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diff --git a/main/generated/torch.sparse_compressed_tensor.html b/main/generated/torch.sparse_compressed_tensor.html
index 706b31ae3e5..fe036d0882c 100644
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diff --git a/main/generated/torch.sparse_coo_tensor.html b/main/generated/torch.sparse_coo_tensor.html
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diff --git a/main/generated/torch.split.html b/main/generated/torch.split.html
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@@ -612,7 +612,7 @@
 <h1>torch.split<a class="headerlink" href="#torch-split" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.split">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_size_or_sections</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.split" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_size_or_sections</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.split" title="Permalink to this definition">¶</a></dt>
 <dd><p>Splits the tensor into chunks. Each chunk is a view of the original tensor.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">split_size_or_sections</span></code> is an integer type, then <a class="reference internal" href="torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code></a> will
 be split into equally sized chunks (if possible). Last chunk will be smaller if
diff --git a/main/generated/torch.sqrt.html b/main/generated/torch.sqrt.html
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diff --git a/main/generated/torch.square.html b/main/generated/torch.square.html
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diff --git a/main/generated/torch.sspaddmm.html b/main/generated/torch.sspaddmm.html
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diff --git a/main/generated/torch.stack.html b/main/generated/torch.stack.html
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diff --git a/main/generated/torch.std.html b/main/generated/torch.std.html
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diff --git a/main/generated/torch.stft.html b/main/generated/torch.stft.html
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@@ -612,7 +612,7 @@
 <h1>torch.stft<a class="headerlink" href="#torch-stft" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.stft">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">stft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#stft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.stft" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">stft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#stft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.stft" title="Permalink to this definition">¶</a></dt>
 <dd><p>Short-time Fourier transform (STFT).</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.sub.html b/main/generated/torch.sub.html
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diff --git a/main/generated/torch.sum.html b/main/generated/torch.sum.html
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diff --git a/main/generated/torch.svd_lowrank.html b/main/generated/torch.svd_lowrank.html
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@@ -612,7 +612,7 @@
 <h1>torch.svd_lowrank<a class="headerlink" href="#torch-svd-lowrank" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.svd_lowrank">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">svd_lowrank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lowrank.html#svd_lowrank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_lowrank.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.svd_lowrank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">svd_lowrank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lowrank.html#svd_lowrank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_lowrank.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.svd_lowrank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the singular value decomposition <code class="docutils literal notranslate"><span class="pre">(U,</span> <span class="pre">S,</span> <span class="pre">V)</span></code> of a matrix,
 batches of matrices, or a sparse matrix <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>A</mi></mrow><annotation encoding="application/x-tex">A</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span></span></span></span></span> such that
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>A</mi><mo>≈</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mtext>H</mtext></msup></mrow><annotation encoding="application/x-tex">A \approx U \operatorname{diag}(S) V^{\text{H}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">≈</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0913em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">H</span></span></span></span></span></span></span></span></span></span></span></span></span></span>. In case <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>M</mi></mrow><annotation encoding="application/x-tex">M</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">M</span></span></span></span></span> is given, then
diff --git a/main/generated/torch.swapaxes.html b/main/generated/torch.swapaxes.html
index e015841e649..54ac2fa4db3 100644
--- a/main/generated/torch.swapaxes.html
+++ b/main/generated/torch.swapaxes.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.swapdims.html b/main/generated/torch.swapdims.html
index f9f12bbf31e..16169e6d674 100644
--- a/main/generated/torch.swapdims.html
+++ b/main/generated/torch.swapdims.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sym_float.html b/main/generated/torch.sym_float.html
index f8fb216341e..11c53141461 100644
--- a/main/generated/torch.sym_float.html
+++ b/main/generated/torch.sym_float.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_float<a class="headerlink" href="#torch-sym-float" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_float">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L805"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L805"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for float casting.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.sym_fresh_size.html b/main/generated/torch.sym_fresh_size.html
index 0bf6d252bea..1c5b66eb555 100644
--- a/main/generated/torch.sym_fresh_size.html
+++ b/main/generated/torch.sym_fresh_size.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_fresh_size<a class="headerlink" href="#torch-sym-fresh-size" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_fresh_size">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_fresh_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_fresh_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_fresh_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_fresh_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_fresh_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_fresh_size" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.sym_int.html b/main/generated/torch.sym_int.html
index b6f5aa3d47f..a4500679409 100644
--- a/main/generated/torch.sym_int.html
+++ b/main/generated/torch.sym_int.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_int<a class="headerlink" href="#torch-sym-int" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_int">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_int" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for int casting.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.sym_ite.html b/main/generated/torch.sym_ite.html
index 469d46c6d84..f0031000b36 100644
--- a/main/generated/torch.sym_ite.html
+++ b/main/generated/torch.sym_ite.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_ite<a class="headerlink" href="#torch-sym-ite" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_ite">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_ite</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_ite"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_ite" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_ite</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_ite"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_ite" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.sym_max.html b/main/generated/torch.sym_max.html
index 0e4535917cb..e775dce560c 100644
--- a/main/generated/torch.sym_max.html
+++ b/main/generated/torch.sym_max.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_max<a class="headerlink" href="#torch-sym-max" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_max">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_max"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_max" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_max"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_max" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for max which avoids branching on a &lt; b.
 Unlike builtins.max(), this only works for int/float, and it always
 promotes to float if any argument is float (unlike builtins.max, which
diff --git a/main/generated/torch.sym_min.html b/main/generated/torch.sym_min.html
index 6235b47388d..d87ebae50ef 100644
--- a/main/generated/torch.sym_min.html
+++ b/main/generated/torch.sym_min.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_min<a class="headerlink" href="#torch-sym-min" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_min">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_min"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L880"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_min" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_min"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L880"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_min" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for min().</p>
 </dd></dl>
 
diff --git a/main/generated/torch.sym_not.html b/main/generated/torch.sym_not.html
index 3e5c3b94dba..7c7f5d2d1a0 100644
--- a/main/generated/torch.sym_not.html
+++ b/main/generated/torch.sym_not.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.sym_not<a class="headerlink" href="#torch-sym-not" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_not">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_not</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_not"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L788"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_not" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_not</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_not"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L788"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_not" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for logical negation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.sym_sum.html b/main/generated/torch.sym_sum.html
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@@ -612,7 +612,7 @@
 <h1>torch.sym_sum<a class="headerlink" href="#torch-sym-sum" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_sum">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L899"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_sum" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L899"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_sum" title="Permalink to this definition">¶</a></dt>
 <dd><p>N-ary add which is faster to compute for long lists than iterated binary
 addition.  Only does something special for integers.</p>
 </dd></dl>
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.tensordot">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">tensordot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#tensordot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L1264"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.tensordot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">tensordot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#tensordot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1264"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.tensordot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a contraction of a and b over multiple dimensions.</p>
 <p><a class="reference internal" href="#torch.tensordot" title="torch.tensordot"><code class="xref py py-attr docutils literal notranslate"><span class="pre">tensordot</span></code></a> implements a generalized matrix product.</p>
 <dl class="field-list simple">
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diff --git a/main/generated/torch.unflatten.html b/main/generated/torch.unflatten.html
index de98cd97477..953885430c8 100644
--- a/main/generated/torch.unflatten.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/generated/torch.unique.html b/main/generated/torch.unique.html
index 70316c4c6df..f4cb49c538e 100644
--- a/main/generated/torch.unique.html
+++ b/main/generated/torch.unique.html
@@ -300,7 +300,7 @@
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.unique<a class="headerlink" href="#torch-unique" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.unique">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unique</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unique" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unique</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unique" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the unique elements of the input tensor.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.unique_consecutive.html b/main/generated/torch.unique_consecutive.html
index 9f827b954f9..20bf7c6d862 100644
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+++ b/main/generated/torch.unique_consecutive.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.unique_consecutive<a class="headerlink" href="#torch-unique-consecutive" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.unique_consecutive">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unique_consecutive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unique_consecutive" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unique_consecutive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unique_consecutive" title="Permalink to this definition">¶</a></dt>
 <dd><p>Eliminates all but the first element from every consecutive group of equivalent elements.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.unravel_index.html b/main/generated/torch.unravel_index.html
index 735cfa0c27a..8f6d97e8386 100644
--- a/main/generated/torch.unravel_index.html
+++ b/main/generated/torch.unravel_index.html
@@ -300,7 +300,7 @@
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     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.unravel_index<a class="headerlink" href="#torch-unravel-index" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.unravel_index">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unravel_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#unravel_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/functional.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unravel_index" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unravel_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#unravel_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unravel_index" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts a tensor of flat indices into a tuple of coordinate tensors that
 index into an arbitrary tensor of the specified shape.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.unsqueeze.html b/main/generated/torch.unsqueeze.html
index 3ae05eff400..404adf6c195 100644
--- a/main/generated/torch.unsqueeze.html
+++ b/main/generated/torch.unsqueeze.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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diff --git a/main/generated/torch.use_deterministic_algorithms.html b/main/generated/torch.use_deterministic_algorithms.html
index 352812198bb..58cbbe27982 100644
--- a/main/generated/torch.use_deterministic_algorithms.html
+++ b/main/generated/torch.use_deterministic_algorithms.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.use_deterministic_algorithms<a class="headerlink" href="#torch-use-deterministic-algorithms" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.use_deterministic_algorithms">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">use_deterministic_algorithms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#use_deterministic_algorithms"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.use_deterministic_algorithms" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">use_deterministic_algorithms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#use_deterministic_algorithms"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.use_deterministic_algorithms" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether PyTorch operations must use “deterministic”
 algorithms. That is, algorithms which, given the same input, and when
 run on the same software and hardware, always produce the same output.
diff --git a/main/generated/torch.utils.generate_methods_for_privateuse1_backend.html b/main/generated/torch.utils.generate_methods_for_privateuse1_backend.html
index 963744ca301..9ebf4de53c4 100644
--- a/main/generated/torch.utils.generate_methods_for_privateuse1_backend.html
+++ b/main/generated/torch.utils.generate_methods_for_privateuse1_backend.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.generate_methods_for_privateuse1_backend<a class="headerlink" href="#torch-utils-generate-methods-for-privateuse1-backend" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.generate_methods_for_privateuse1_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">generate_methods_for_privateuse1_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">for_tensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_packed_sequence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsupported_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/backend_registration.html#generate_methods_for_privateuse1_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/backend_registration.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.generate_methods_for_privateuse1_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">generate_methods_for_privateuse1_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">for_tensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_packed_sequence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsupported_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/backend_registration.html#generate_methods_for_privateuse1_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/backend_registration.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.generate_methods_for_privateuse1_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Automatically generate attributes and methods for the custom backend after rename privateuse1 backend.</p>
 <p>In the default scenario, storage-related methods will not be generated automatically.</p>
 <p>When you implement kernels for various torch operations, and register them to the PrivateUse1 dispatch key.
diff --git a/main/generated/torch.utils.get_cpp_backtrace.html b/main/generated/torch.utils.get_cpp_backtrace.html
index 1d7b056afcb..d94b9957c88 100644
--- a/main/generated/torch.utils.get_cpp_backtrace.html
+++ b/main/generated/torch.utils.get_cpp_backtrace.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.get_cpp_backtrace<a class="headerlink" href="#torch-utils-get-cpp-backtrace" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.get_cpp_backtrace">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">get_cpp_backtrace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">frames_to_skip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum_number_of_frames</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/cpp_backtrace.html#get_cpp_backtrace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/cpp_backtrace.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.get_cpp_backtrace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">get_cpp_backtrace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">frames_to_skip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum_number_of_frames</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/cpp_backtrace.html#get_cpp_backtrace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_backtrace.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.get_cpp_backtrace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a string containing the C++ stack trace of the current thread.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.utils.rename_privateuse1_backend.html b/main/generated/torch.utils.rename_privateuse1_backend.html
index 8c27280ef15..72c356093dc 100644
--- a/main/generated/torch.utils.rename_privateuse1_backend.html
+++ b/main/generated/torch.utils.rename_privateuse1_backend.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.rename_privateuse1_backend<a class="headerlink" href="#torch-utils-rename-privateuse1-backend" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.rename_privateuse1_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">rename_privateuse1_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/backend_registration.html#rename_privateuse1_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/backend_registration.py#L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.rename_privateuse1_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">rename_privateuse1_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/backend_registration.html#rename_privateuse1_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/backend_registration.py#L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.rename_privateuse1_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Rename the privateuse1 backend device to make it more convenient to use as a device name within PyTorch APIs.</p>
 <p>The steps are:</p>
 <ol class="arabic simple">
diff --git a/main/generated/torch.utils.set_module.html b/main/generated/torch.utils.set_module.html
index 46f4bdbb71c..2baf1121f9e 100644
--- a/main/generated/torch.utils.set_module.html
+++ b/main/generated/torch.utils.set_module.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.set_module<a class="headerlink" href="#torch-utils-set-module" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.set_module">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">set_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils.html#set_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/__init__.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.set_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">set_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils.html#set_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/__init__.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.set_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module attribute on a python object for a given object for nicer printing</p>
 </dd></dl>
 
diff --git a/main/generated/torch.utils.swap_tensors.html b/main/generated/torch.utils.swap_tensors.html
index 0277ebc935a..a42411d475f 100644
--- a/main/generated/torch.utils.swap_tensors.html
+++ b/main/generated/torch.utils.swap_tensors.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.swap_tensors<a class="headerlink" href="#torch-utils-swap-tensors" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.swap_tensors">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">swap_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils.html#swap_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/__init__.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.swap_tensors" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">swap_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils.html#swap_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/__init__.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.swap_tensors" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function swaps the content of the two Tensor objects.
 At a high level, this will make t1 have the content of t2 while preserving
 its identity.</p>
diff --git a/main/generated/torch.vander.html b/main/generated/torch.vander.html
index 929b9baae6b..419f0740519 100644
--- a/main/generated/torch.vander.html
+++ b/main/generated/torch.vander.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.var.html b/main/generated/torch.var.html
index 895d23a020c..06cece7d8a4 100644
--- a/main/generated/torch.var.html
+++ b/main/generated/torch.var.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.var_mean.html b/main/generated/torch.var_mean.html
index a8ed835cf2d..5cedaffd916 100644
--- a/main/generated/torch.var_mean.html
+++ b/main/generated/torch.var_mean.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.vdot.html b/main/generated/torch.vdot.html
index 945ffdbfdc7..01a6899ca50 100644
--- a/main/generated/torch.vdot.html
+++ b/main/generated/torch.vdot.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.view_as_complex.html b/main/generated/torch.view_as_complex.html
index 7f3cb8482ad..1abafea8aff 100644
--- a/main/generated/torch.view_as_complex.html
+++ b/main/generated/torch.view_as_complex.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.view_as_real.html b/main/generated/torch.view_as_real.html
index f3310f87254..c312678a689 100644
--- a/main/generated/torch.view_as_real.html
+++ b/main/generated/torch.view_as_real.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.vmap.html b/main/generated/torch.vmap.html
index df507a7a906..53d20924316 100644
--- a/main/generated/torch.vmap.html
+++ b/main/generated/torch.vmap.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.vmap<a class="headerlink" href="#torch-vmap" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.vmap">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_functorch/apis.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.vmap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/apis.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>vmap is the vectorizing map; <code class="docutils literal notranslate"><span class="pre">vmap(func)</span></code> returns a new function that
 maps <code class="docutils literal notranslate"><span class="pre">func</span></code> over some dimension of the inputs. Semantically, vmap
 pushes the map into PyTorch operations called by <code class="docutils literal notranslate"><span class="pre">func</span></code>, effectively
diff --git a/main/generated/torch.vsplit.html b/main/generated/torch.vsplit.html
index 4aa116af12f..bf92e1069f7 100644
--- a/main/generated/torch.vsplit.html
+++ b/main/generated/torch.vsplit.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.vstack.html b/main/generated/torch.vstack.html
index c77562e1c0d..596ec71adcc 100644
--- a/main/generated/torch.vstack.html
+++ b/main/generated/torch.vstack.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.where.html b/main/generated/torch.where.html
index 8958f72ff88..9baf7fec8a5 100644
--- a/main/generated/torch.where.html
+++ b/main/generated/torch.where.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.xlogy.html b/main/generated/torch.xlogy.html
index 26430c3bec4..3a412cabe93 100644
--- a/main/generated/torch.xlogy.html
+++ b/main/generated/torch.xlogy.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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diff --git a/main/generated/torch.xpu.Event.html b/main/generated/torch.xpu.Event.html
index f3f29070105..2f2779e2f83 100644
--- a/main/generated/torch.xpu.Event.html
+++ b/main/generated/torch.xpu.Event.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.Event">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a XPU event.</p>
 <p>XPU events are synchronization markers that can be used to monitor the
 device’s progress, and to synchronize XPU streams.</p>
@@ -627,7 +627,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.elapsed_time">
-<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the time elapsed.</p>
 <p>Time reported in milliseconds after the event was recorded and
 before the end_event was recorded.</p>
@@ -635,7 +635,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all work currently captured by event has completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -650,7 +650,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.record">
-<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.record" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record the event in a given stream.</p>
 <p>Uses <code class="docutils literal notranslate"><span class="pre">torch.xpu.current_stream()</span></code> if no stream is specified. The
 stream’s device must match the event’s device.</p>
@@ -660,7 +660,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for the event to complete.</p>
 <p>Waits until the completion of all work currently captured in this event.
 This prevents the CPU thread from proceeding until the event completes.</p>
@@ -670,7 +670,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the given stream wait for this event.</p>
 <p>Use <code class="docutils literal notranslate"><span class="pre">torch.xpu.current_stream()</span></code> if no stream is specified.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.xpu.Stream.html b/main/generated/torch.xpu.Stream.html
index 4a55e9c3fec..7479a98909e 100644
--- a/main/generated/torch.xpu.Stream.html
+++ b/main/generated/torch.xpu.Stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.Stream">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a XPU stream.</p>
 <p>A XPU stream is a linear sequence of execution that belongs to a specific
 device, independent from other streams. It supports with statement as a
@@ -634,7 +634,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all the work submitted has been completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -648,7 +648,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.record_event">
-<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.record_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.record_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.record_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.record_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -663,7 +663,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all the kernels in this stream to complete.</p>
 <dl class="field-list simple">
 </dl>
@@ -671,7 +671,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.wait_event">
-<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.wait_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.wait_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.wait_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.wait_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the stream wait for an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -682,7 +682,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.wait_stream">
-<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.wait_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/streams.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.wait_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.wait_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.wait_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize with another stream.</p>
 <p>All future work submitted to this stream will wait until all kernels
 submitted to a given stream at the time of call complete.</p>
diff --git a/main/generated/torch.xpu.StreamContext.html b/main/generated/torch.xpu.StreamContext.html
index bb1ea7194e6..cf9bf2d9b57 100644
--- a/main/generated/torch.xpu.StreamContext.html
+++ b/main/generated/torch.xpu.StreamContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StreamContext<a class="headerlink" href="#streamcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.StreamContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.StreamContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.StreamContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that selects a given stream.</p>
 <p>All XPU kernels queued within its context will be enqueued on a selected
 stream.</p>
diff --git a/main/generated/torch.xpu.current_device.html b/main/generated/torch.xpu.current_device.html
index fc8844ed22f..32f37cb370a 100644
--- a/main/generated/torch.xpu.current_device.html
+++ b/main/generated/torch.xpu.current_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.current_device<a class="headerlink" href="#torch-xpu-current-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.current_device">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.current_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.current_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.current_stream.html b/main/generated/torch.xpu.current_stream.html
index e9b87777449..d1d3e952c02 100644
--- a/main/generated/torch.xpu.current_stream.html
+++ b/main/generated/torch.xpu.current_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.current_stream<a class="headerlink" href="#torch-xpu-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the currently selected <a class="reference internal" href="torch.xpu.Stream.html#torch.xpu.Stream" title="torch.xpu.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.device.html b/main/generated/torch.xpu.device.html
index 079979bfd43..2998cba73bf 100644
--- a/main/generated/torch.xpu.device.html
+++ b/main/generated/torch.xpu.device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>device<a class="headerlink" href="#device" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.device">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.device_count.html b/main/generated/torch.xpu.device_count.html
index 3166c66f283..efbaccec266 100644
--- a/main/generated/torch.xpu.device_count.html
+++ b/main/generated/torch.xpu.device_count.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.device_count<a class="headerlink" href="#torch-xpu-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of XPU device available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.device_of.html b/main/generated/torch.xpu.device_of.html
index 5ed33e89e0f..b4a5a33daa7 100644
--- a/main/generated/torch.xpu.device_of.html
+++ b/main/generated/torch.xpu.device_of.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>device_of<a class="headerlink" href="#device-of" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.device_of">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device_of" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device_of" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the current device to that of given object.</p>
 <p>You can use both tensors and storages as arguments. If a given object is
 not allocated on a XPU, this is a no-op.</p>
diff --git a/main/generated/torch.xpu.empty_cache.html b/main/generated/torch.xpu.empty_cache.html
index 6bf5c6a0675..7cac1376a52 100644
--- a/main/generated/torch.xpu.empty_cache.html
+++ b/main/generated/torch.xpu.empty_cache.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.empty_cache<a class="headerlink" href="#torch-xpu-empty-cache" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.empty_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.empty_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.empty_cache" title="Permalink to this definition">¶</a></dt>
 <dd><p>Release all unoccupied cached memory currently held by the caching
 allocator so that those can be used in other XPU application.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.xpu.get_arch_list.html b/main/generated/torch.xpu.get_arch_list.html
index 591dffcc492..b1e2c904812 100644
--- a/main/generated/torch.xpu.get_arch_list.html
+++ b/main/generated/torch.xpu.get_arch_list.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_arch_list<a class="headerlink" href="#torch-xpu-get-arch-list" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_arch_list">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_arch_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_arch_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_arch_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_arch_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_arch_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_arch_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return list XPU architectures this library was compiled for.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.get_device_capability.html b/main/generated/torch.xpu.get_device_capability.html
index cd4199d0a8d..74b98161ffe 100644
--- a/main/generated/torch.xpu.get_device_capability.html
+++ b/main/generated/torch.xpu.get_device_capability.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_device_capability<a class="headerlink" href="#torch-xpu-get-device-capability" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_device_capability">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_capability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_capability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the xpu capability of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.get_device_name.html b/main/generated/torch.xpu.get_device_name.html
index eb8859dc4d7..939de46e95a 100644
--- a/main/generated/torch.xpu.get_device_name.html
+++ b/main/generated/torch.xpu.get_device_name.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_device_name<a class="headerlink" href="#torch-xpu-get-device-name" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_device_name">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the name of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.get_device_properties.html b/main/generated/torch.xpu.get_device_properties.html
index ec8db695777..6debeb28a03 100644
--- a/main/generated/torch.xpu.get_device_properties.html
+++ b/main/generated/torch.xpu.get_device_properties.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_device_properties<a class="headerlink" href="#torch-xpu-get-device-properties" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_device_properties">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_properties</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_properties"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_properties" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_properties</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_properties"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_properties" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the properties of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.get_gencode_flags.html b/main/generated/torch.xpu.get_gencode_flags.html
index 37ae1f56e82..5dd170a4361 100644
--- a/main/generated/torch.xpu.get_gencode_flags.html
+++ b/main/generated/torch.xpu.get_gencode_flags.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_gencode_flags<a class="headerlink" href="#torch-xpu-get-gencode-flags" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_gencode_flags">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_gencode_flags</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_gencode_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_gencode_flags" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_gencode_flags</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_gencode_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_gencode_flags" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return XPU AOT(ahead-of-time) build flags this library was compiled with.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.get_rng_state.html b/main/generated/torch.xpu.get_rng_state.html
index b0011b129b5..340653a5b1a 100644
--- a/main/generated/torch.xpu.get_rng_state.html
+++ b/main/generated/torch.xpu.get_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_rng_state<a class="headerlink" href="#torch-xpu-get-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/random.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the random number generator state of the specified GPU as a ByteTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.get_rng_state_all.html b/main/generated/torch.xpu.get_rng_state_all.html
index 1bc31ba4d6e..6a344bbe9c3 100644
--- a/main/generated/torch.xpu.get_rng_state_all.html
+++ b/main/generated/torch.xpu.get_rng_state_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_rng_state_all<a class="headerlink" href="#torch-xpu-get-rng-state-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_rng_state_all">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_rng_state_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#get_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/random.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_rng_state_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_rng_state_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#get_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_rng_state_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of ByteTensor representing the random number states of all devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.get_stream_from_external.html b/main/generated/torch.xpu.get_stream_from_external.html
index 27d9dd7e164..26e296b9250 100644
--- a/main/generated/torch.xpu.get_stream_from_external.html
+++ b/main/generated/torch.xpu.get_stream_from_external.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_stream_from_external<a class="headerlink" href="#torch-xpu-get-stream-from-external" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_stream_from_external">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_stream_from_external</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_stream_from_external"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_stream_from_external" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_stream_from_external</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_stream_from_external"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_stream_from_external" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a <a class="reference internal" href="torch.xpu.Stream.html#torch.xpu.Stream" title="torch.xpu.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> from an external SYCL queue.</p>
 <p>This function is used to wrap SYCL queue created in other libraries in order
 to facilitate data exchange and multi-library interactions.</p>
diff --git a/main/generated/torch.xpu.init.html b/main/generated/torch.xpu.init.html
index 6526392dcf5..99e6b725a7b 100644
--- a/main/generated/torch.xpu.init.html
+++ b/main/generated/torch.xpu.init.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.init<a class="headerlink" href="#torch-xpu-init" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.init">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.init" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.init" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize PyTorch’s XPU state.
 This is a Python API about lazy initialization that avoids initializing
 XPU until the first time it is accessed. Does nothing if the XPU state is
diff --git a/main/generated/torch.xpu.initial_seed.html b/main/generated/torch.xpu.initial_seed.html
index 0b706c489b1..4bed9634e63 100644
--- a/main/generated/torch.xpu.initial_seed.html
+++ b/main/generated/torch.xpu.initial_seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.initial_seed<a class="headerlink" href="#torch-xpu-initial-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.initial_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/random.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.initial_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.initial_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current random seed of the current GPU.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.xpu.is_available.html b/main/generated/torch.xpu.is_available.html
index ac18d0d1754..ff58d392e43 100644
--- a/main/generated/torch.xpu.is_available.html
+++ b/main/generated/torch.xpu.is_available.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.xpu.is_available<a class="headerlink" href="#torch-xpu-is-available" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if XPU is currently available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.is_initialized.html b/main/generated/torch.xpu.is_initialized.html
index 040cbe010ad..16faa67c645 100644
--- a/main/generated/torch.xpu.is_initialized.html
+++ b/main/generated/torch.xpu.is_initialized.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.is_initialized<a class="headerlink" href="#torch-xpu-is-initialized" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.is_initialized">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.is_initialized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.is_initialized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch’s XPU state has been initialized.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.xpu.manual_seed.html b/main/generated/torch.xpu.manual_seed.html
index 2428b9f6846..d35084e7a1e 100644
--- a/main/generated/torch.xpu.manual_seed.html
+++ b/main/generated/torch.xpu.manual_seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.manual_seed<a class="headerlink" href="#torch-xpu-manual-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/random.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers for the current GPU.</p>
 <p>It’s safe to call this function if XPU is not available; in that case, it is silently ignored.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.xpu.manual_seed_all.html b/main/generated/torch.xpu.manual_seed_all.html
index 725d484dbfc..10a6931c361 100644
--- a/main/generated/torch.xpu.manual_seed_all.html
+++ b/main/generated/torch.xpu.manual_seed_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.manual_seed_all<a class="headerlink" href="#torch-xpu-manual-seed-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.manual_seed_all">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">manual_seed_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#manual_seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/random.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.manual_seed_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">manual_seed_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#manual_seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.manual_seed_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers on all GPUs.</p>
 <p>It’s safe to call this function if XPU is not available; in that case, it is silently ignored.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.xpu.max_memory_allocated.html b/main/generated/torch.xpu.max_memory_allocated.html
index a076608d65a..41e47e1d7d9 100644
--- a/main/generated/torch.xpu.max_memory_allocated.html
+++ b/main/generated/torch.xpu.max_memory_allocated.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.max_memory_allocated<a class="headerlink" href="#torch-xpu-max-memory-allocated" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.max_memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.max_memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.max_memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the maximum GPU memory occupied by tensors in bytes for a given device.</p>
 <p>By default, this returns the peak allocated memory since the beginning of
 this program. <a class="reference internal" href="torch.xpu.reset_peak_memory_stats.html#torch.xpu.reset_peak_memory_stats" title="torch.xpu.reset_peak_memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">reset_peak_memory_stats()</span></code></a> can be used to
diff --git a/main/generated/torch.xpu.max_memory_reserved.html b/main/generated/torch.xpu.max_memory_reserved.html
index fdf1fb8ef32..6ffb86deec5 100644
--- a/main/generated/torch.xpu.max_memory_reserved.html
+++ b/main/generated/torch.xpu.max_memory_reserved.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.max_memory_reserved<a class="headerlink" href="#torch-xpu-max-memory-reserved" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.max_memory_reserved">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">max_memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#max_memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.max_memory_reserved" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">max_memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#max_memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.max_memory_reserved" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the maximum GPU memory managed by the caching allocator in bytes for a given device.</p>
 <p>By default, this returns the peak cached memory since the beginning of this
 program. <a class="reference internal" href="torch.xpu.reset_peak_memory_stats.html#torch.xpu.reset_peak_memory_stats" title="torch.xpu.reset_peak_memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">reset_peak_memory_stats()</span></code></a> can be used to reset
diff --git a/main/generated/torch.xpu.mem_get_info.html b/main/generated/torch.xpu.mem_get_info.html
index b5b9cba3fe1..263e6c7733c 100644
--- a/main/generated/torch.xpu.mem_get_info.html
+++ b/main/generated/torch.xpu.mem_get_info.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.mem_get_info<a class="headerlink" href="#torch-xpu-mem-get-info" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.mem_get_info">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">mem_get_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#mem_get_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.mem_get_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">mem_get_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#mem_get_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.mem_get_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the global free and total GPU memory for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.memory_allocated.html b/main/generated/torch.xpu.memory_allocated.html
index 3d1df686290..f76c067e1b1 100644
--- a/main/generated/torch.xpu.memory_allocated.html
+++ b/main/generated/torch.xpu.memory_allocated.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.memory_allocated<a class="headerlink" href="#torch-xpu-memory-allocated" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current GPU memory occupied by tensors in bytes for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.memory_reserved.html b/main/generated/torch.xpu.memory_reserved.html
index 65fc53ccb28..798430bf156 100644
--- a/main/generated/torch.xpu.memory_reserved.html
+++ b/main/generated/torch.xpu.memory_reserved.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.xpu.memory_reserved<a class="headerlink" href="#torch-xpu-memory-reserved" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.memory_reserved">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_reserved" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_reserved" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current GPU memory managed by the caching allocator in bytes for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.memory_stats.html b/main/generated/torch.xpu.memory_stats.html
index b1cbab8b95c..a5cd35808d6 100644
--- a/main/generated/torch.xpu.memory_stats.html
+++ b/main/generated/torch.xpu.memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.xpu.memory_stats<a class="headerlink" href="#torch-xpu-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of XPU memory allocator statistics for a given device.</p>
 <p>The return value of this function is a dictionary of statistics, each of
 which is a non-negative integer.</p>
diff --git a/main/generated/torch.xpu.memory_stats_as_nested_dict.html b/main/generated/torch.xpu.memory_stats_as_nested_dict.html
index d41fea880cd..786bdde282f 100644
--- a/main/generated/torch.xpu.memory_stats_as_nested_dict.html
+++ b/main/generated/torch.xpu.memory_stats_as_nested_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.memory_stats_as_nested_dict<a class="headerlink" href="#torch-xpu-memory-stats-as-nested-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.memory_stats_as_nested_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_stats_as_nested_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_stats_as_nested_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_stats_as_nested_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_stats_as_nested_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_stats_as_nested_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_stats_as_nested_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the result of <a class="reference internal" href="torch.xpu.memory_stats.html#torch.xpu.memory_stats" title="torch.xpu.memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> as a nested dictionary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.reset_accumulated_memory_stats.html b/main/generated/torch.xpu.reset_accumulated_memory_stats.html
index d3ae5ec684c..ef6a2f9a918 100644
--- a/main/generated/torch.xpu.reset_accumulated_memory_stats.html
+++ b/main/generated/torch.xpu.reset_accumulated_memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.reset_accumulated_memory_stats<a class="headerlink" href="#torch-xpu-reset-accumulated-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.reset_accumulated_memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">reset_accumulated_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#reset_accumulated_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.reset_accumulated_memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">reset_accumulated_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#reset_accumulated_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.reset_accumulated_memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the “accumulated” (historical) stats tracked by the XPU memory allocator.</p>
 <p>See <a class="reference internal" href="torch.xpu.memory_stats.html#torch.xpu.memory_stats" title="torch.xpu.memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> for details. Accumulated stats correspond to
 the <cite>“allocated”</cite> and <cite>“freed”</cite> keys in each individual stat dict.</p>
diff --git a/main/generated/torch.xpu.reset_peak_memory_stats.html b/main/generated/torch.xpu.reset_peak_memory_stats.html
index 939f26e7e43..2efa8df1273 100644
--- a/main/generated/torch.xpu.reset_peak_memory_stats.html
+++ b/main/generated/torch.xpu.reset_peak_memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.reset_peak_memory_stats<a class="headerlink" href="#torch-xpu-reset-peak-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.reset_peak_memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">reset_peak_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#reset_peak_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/memory.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.reset_peak_memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">reset_peak_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#reset_peak_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.reset_peak_memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the “peak” stats tracked by the XPU memory allocator.</p>
 <p>See <a class="reference internal" href="torch.xpu.memory_stats.html#torch.xpu.memory_stats" title="torch.xpu.memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> for details. Peak stats correspond to the
 <cite>“peak”</cite> key in each individual stat dict.</p>
diff --git a/main/generated/torch.xpu.seed.html b/main/generated/torch.xpu.seed.html
index a4162fbc4d5..8f2dded5f92 100644
--- a/main/generated/torch.xpu.seed.html
+++ b/main/generated/torch.xpu.seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.seed<a class="headerlink" href="#torch-xpu-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/random.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers to a random number for the current GPU.</p>
 <p>It’s safe to call this function if XPU is not available; in that case, it is silently ignored.</p>
 <div class="admonition warning">
diff --git a/main/generated/torch.xpu.seed_all.html b/main/generated/torch.xpu.seed_all.html
index b4517d44a7b..083cff8bb56 100644
--- a/main/generated/torch.xpu.seed_all.html
+++ b/main/generated/torch.xpu.seed_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.xpu.seed_all<a class="headerlink" href="#torch-xpu-seed-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.seed_all">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">seed_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/random.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.seed_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">seed_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.seed_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers to a random number on all GPUs.</p>
 <p>It’s safe to call this function if XPU is not available; in that case, it is silently ignored.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.xpu.set_device.html b/main/generated/torch.xpu.set_device.html
index 733a1633a4e..aaa7cb89606 100644
--- a/main/generated/torch.xpu.set_device.html
+++ b/main/generated/torch.xpu.set_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.set_device<a class="headerlink" href="#torch-xpu-set-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.set_device">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.set_rng_state.html b/main/generated/torch.xpu.set_rng_state.html
index 2e63ff4446c..7268ee408a7 100644
--- a/main/generated/torch.xpu.set_rng_state.html
+++ b/main/generated/torch.xpu.set_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.set_rng_state<a class="headerlink" href="#torch-xpu-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/random.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the random number generator state of the specified GPU.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.set_rng_state_all.html b/main/generated/torch.xpu.set_rng_state_all.html
index 4a91dcec290..37cfbc4f12d 100644
--- a/main/generated/torch.xpu.set_rng_state_all.html
+++ b/main/generated/torch.xpu.set_rng_state_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.set_rng_state_all<a class="headerlink" href="#torch-xpu-set-rng-state-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.set_rng_state_all">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_rng_state_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#set_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/random.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_rng_state_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_rng_state_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#set_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_rng_state_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the random number generator state of all devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.set_stream.html b/main/generated/torch.xpu.set_stream.html
index a485c0b57e0..fdeef226f0f 100644
--- a/main/generated/torch.xpu.set_stream.html
+++ b/main/generated/torch.xpu.set_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.set_stream<a class="headerlink" href="#torch-xpu-set-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.set_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_stream" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Set the current stream.This is a wrapper API to set the stream.</dt><dd><p>Usage of this function is discouraged in favor of the <code class="docutils literal notranslate"><span class="pre">stream</span></code>
 context manager.</p>
diff --git a/main/generated/torch.xpu.stream.html b/main/generated/torch.xpu.stream.html
index 7580544cec0..a356e368c2d 100644
--- a/main/generated/torch.xpu.stream.html
+++ b/main/generated/torch.xpu.stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.stream<a class="headerlink" href="#torch-xpu-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.stream">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap around the Context-manager StreamContext that selects a given stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.synchronize.html b/main/generated/torch.xpu.synchronize.html
index 8520551bc54..adb0c0302cd 100644
--- a/main/generated/torch.xpu.synchronize.html
+++ b/main/generated/torch.xpu.synchronize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.synchronize<a class="headerlink" href="#torch-xpu-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/xpu/__init__.py#L408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all kernels in all streams on a XPU device to complete.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.zeros.html b/main/generated/torch.zeros.html
index 14190be7ebe..6c5c0a565b5 100644
--- a/main/generated/torch.zeros.html
+++ b/main/generated/torch.zeros.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.zeros_like.html b/main/generated/torch.zeros_like.html
index f6bf369c3a6..7fe264b26cc 100644
--- a/main/generated/torch.zeros_like.html
+++ b/main/generated/torch.zeros_like.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/genindex.html b/main/genindex.html
index 342b6942a32..4d42b1381ef 100644
--- a/main/genindex.html
+++ b/main/genindex.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/hub.html b/main/hub.html
index 2d3c81ea6b1..6947e7ea9d3 100644
--- a/main/hub.html
+++ b/main/hub.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -683,7 +683,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 <a class="reference internal" href="#torch.hub.load" title="torch.hub.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.hub.load()</span></code></a>.</p>
 <span class="target" id="module-torch.hub"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.list">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">github</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/hub.py#L422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">github</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.list" title="Permalink to this definition">¶</a></dt>
 <dd><p>List all callable entrypoints available in the repo specified by <code class="docutils literal notranslate"><span class="pre">github</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -736,7 +736,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.help">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">help</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">github</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#help"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/hub.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.help" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">help</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">github</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#help"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.help" title="Permalink to this definition">¶</a></dt>
 <dd><p>Show the docstring of entrypoint <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -781,7 +781,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.load">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">repo_or_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'github'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/hub.py#L549"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">repo_or_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'github'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L549"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a model from a github repo or a local directory.</p>
 <p>Note: Loading a model is the typical use case, but this can also be used to
 for loading other objects such as tokenizers, loss functions, etc.</p>
@@ -855,7 +855,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.download_url_to_file">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">download_url_to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hash_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#download_url_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/hub.py#L681"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.download_url_to_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">download_url_to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hash_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#download_url_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L681"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.download_url_to_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Download object at the given URL to a local path.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -880,7 +880,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.load_state_dict_from_url">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">load_state_dict_from_url</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#load_state_dict_from_url"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/hub.py#L803"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.load_state_dict_from_url" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">load_state_dict_from_url</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#load_state_dict_from_url"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L803"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.load_state_dict_from_url" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads the Torch serialized object at the given URL.</p>
 <p>If downloaded file is a zip file, it will be automatically
 decompressed.</p>
@@ -943,7 +943,7 @@ <h3>Where are my downloaded models saved?<a class="headerlink" href="#where-are-
 </ul>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.get_dir">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">get_dir</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#get_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/hub.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.get_dir" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">get_dir</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#get_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.get_dir" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the Torch Hub cache directory used for storing downloaded models &amp; weights.</p>
 <p>If <a class="reference internal" href="#torch.hub.set_dir" title="torch.hub.set_dir"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_dir()</span></code></a> is not called, default path is <code class="docutils literal notranslate"><span class="pre">$TORCH_HOME/hub</span></code> where
 environment variable <code class="docutils literal notranslate"><span class="pre">$TORCH_HOME</span></code> defaults to <code class="docutils literal notranslate"><span class="pre">$XDG_CACHE_HOME/torch</span></code>.
@@ -959,7 +959,7 @@ <h3>Where are my downloaded models saved?<a class="headerlink" href="#where-are-
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.set_dir">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">set_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#set_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/hub.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.set_dir" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">set_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#set_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.set_dir" title="Permalink to this definition">¶</a></dt>
 <dd><p>Optionally set the Torch Hub directory used to save downloaded models &amp; weights.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/index.html b/main/index.html
index 7164d8f5c23..2e908f5a81b 100644
--- a/main/index.html
+++ b/main/index.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit.html b/main/jit.html
index a09bd5e67e6..7ee9ec906ea 100644
--- a/main/jit.html
+++ b/main/jit.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1271,7 +1271,7 @@ <h4>Modules<a class="headerlink" href="#modules" title="Permalink to this headin
 <code class="docutils literal notranslate"><span class="pre">&#64;torch.jit.export</span></code> methods.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.export">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_jit_internal.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_jit_internal.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.export" title="Permalink to this definition">¶</a></dt>
 <dd><p>This decorator indicates that a method on an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> is used as an entry point into a
 <a class="reference internal" href="generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> and should be compiled.</p>
 <p><code class="docutils literal notranslate"><span class="pre">forward</span></code> implicitly is assumed to be an entry point, so it does not need this decorator.
diff --git a/main/jit_builtin_functions.html b/main/jit_builtin_functions.html
index ae4886e3a70..1cccc8cca51 100644
--- a/main/jit_builtin_functions.html
+++ b/main/jit_builtin_functions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit_language_reference.html b/main/jit_language_reference.html
index 144e0118b78..2eb4f2fee76 100644
--- a/main/jit_language_reference.html
+++ b/main/jit_language_reference.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1363,7 +1363,7 @@ <h3>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 check the correctness of the model as you go.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.is_scripting">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">is_scripting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_jit_internal.html#is_scripting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_jit_internal.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.is_scripting" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">is_scripting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_jit_internal.html#is_scripting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.is_scripting" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function that returns True when in compilation and False otherwise. This
 is useful especially with the &#64;unused decorator to leave code in your
 model that is not yet TorchScript compatible.
@@ -1390,7 +1390,7 @@ <h3>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.is_tracing">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">is_tracing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/jit/_trace.html#is_tracing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/jit/_trace.py#L1324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.is_tracing" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">is_tracing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/jit/_trace.html#is_tracing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_trace.py#L1324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.is_tracing" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a boolean value.</p>
 <p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> in tracing (if a function is called during the
 tracing of code with <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code>) and <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
diff --git a/main/jit_language_reference_v2.html b/main/jit_language_reference_v2.html
index 13c73eaf011..2fc5e4da13a 100644
--- a/main/jit_language_reference_v2.html
+++ b/main/jit_language_reference_v2.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit_python_reference.html b/main/jit_python_reference.html
index b8970ae5cbe..9226f4a19ad 100644
--- a/main/jit_python_reference.html
+++ b/main/jit_python_reference.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit_unsupported.html b/main/jit_unsupported.html
index 7d9291fcc0e..88165a78cf5 100644
--- a/main/jit_unsupported.html
+++ b/main/jit_unsupported.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit_utils.html b/main/jit_utils.html
index 487e6f0434d..939a5bc3184 100644
--- a/main/jit_utils.html
+++ b/main/jit_utils.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/library.html b/main/library.html
index 773e86ccd50..8f75ad27a6b 100644
--- a/main/library.html
+++ b/main/library.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -623,7 +623,7 @@ <h2>Testing custom ops<a class="headerlink" href="#testing-custom-ops" title="Pe
 mathematically correct.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.opcheck">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">opcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_utils</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('test_schema',</span> <span class="pre">'test_autograd_registration',</span> <span class="pre">'test_faketensor',</span> <span class="pre">'test_aot_dispatch_dynamic')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#opcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.opcheck" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">opcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_utils</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('test_schema',</span> <span class="pre">'test_autograd_registration',</span> <span class="pre">'test_faketensor',</span> <span class="pre">'test_aot_dispatch_dynamic')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#opcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.opcheck" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given an operator and some sample arguments, tests if the operator is
 registered correctly.</p>
 <p>That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
@@ -733,7 +733,7 @@ <h2>Creating new custom ops in Python<a class="headerlink" href="#creating-new-c
 <p>Use <a class="reference internal" href="#torch.library.custom_op" title="torch.library.custom_op"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.custom_op()</span></code></a> to create new custom ops.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.custom_op">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">custom_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_library/custom_ops.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.custom_op" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">custom_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/custom_ops.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.custom_op" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wraps a function into custom operator.</p>
 <p>Reasons why you may want to create a custom op include:
 - Wrapping a third-party library or custom kernel to work with PyTorch
@@ -831,7 +831,7 @@ <h2>Creating new custom ops in Python<a class="headerlink" href="#creating-new-c
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.triton_op">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">triton_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_library/triton.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.triton_op" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">triton_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/triton.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.triton_op" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a custom operator whose implementation is backed by 1+ triton kernels.</p>
 <p>This is a more structured way of using triton kernels with PyTorch.
 Prefer using triton kernels with no <code class="docutils literal notranslate"><span class="pre">torch.library</span></code> custom operator wrappers
@@ -925,7 +925,7 @@ <h2>Creating new custom ops in Python<a class="headerlink" href="#creating-new-c
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.wrap_triton">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">wrap_triton</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">triton_kernel</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_library/triton.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.wrap_triton" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">wrap_triton</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">triton_kernel</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/triton.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.wrap_triton" title="Permalink to this definition">¶</a></dt>
 <dd><p>Allows capture of a triton kernel into a graph via make_fx or
 non-strict <code class="docutils literal notranslate"><span class="pre">torch.export</span></code>.</p>
 <p>These technologies perform Dispatcher-based tracing (via
@@ -998,7 +998,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 via PyTorch’s C++ operator registration APIs).</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_kernel">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_types</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_kernel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_types</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_kernel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an implementation for a device type for this operator.</p>
 <p>Some valid device_types are: “cpu”, “cuda”, “xla”, “mps”, “ipu”, “xpu”.
 This API may be used as a decorator.</p>
@@ -1047,7 +1047,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_autocast">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_autocast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_autocast" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an autocast dispatch rule for this custom op.</p>
 <p>Valid <cite>device_type</cite> include: “cpu” and “cuda”.</p>
 <dl class="field-list simple">
@@ -1089,7 +1089,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_autograd">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_autograd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_autograd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_autograd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_autograd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_autograd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_autograd" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward formula for this custom op.</p>
 <p>In order for an operator to work with autograd, you need to register
 a backward formula:
@@ -1173,7 +1173,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_fake">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_fake</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_fake"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L912"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_fake" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_fake</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_fake"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L912"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_fake" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a FakeTensor implementation (“fake impl”) for this operator.</p>
 <p>Also sometimes known as a “meta kernel”, “abstract impl”.</p>
 <p>An “FakeTensor implementation” specifies the behavior of this operator on
@@ -1254,7 +1254,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_vmap">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_vmap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L1244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_vmap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_vmap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a vmap implementation to support <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a> for this custom op.</p>
 <p>This API may be used as a decorator (see examples).</p>
 <p>In order for an operator to work with <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>, you may need to register a
@@ -1332,14 +1332,14 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.impl_abstract">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl_abstract</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl_abstract"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L744"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl_abstract" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl_abstract</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl_abstract"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L744"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl_abstract" title="Permalink to this definition">¶</a></dt>
 <dd><p>This API was renamed to <a class="reference internal" href="#torch.library.register_fake" title="torch.library.register_fake"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.register_fake()</span></code></a> in PyTorch 2.4.
 Please use that instead.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.get_ctx">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">get_ctx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#get_ctx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L1428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.get_ctx" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">get_ctx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#get_ctx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.get_ctx" title="Permalink to this definition">¶</a></dt>
 <dd><p>get_ctx() returns the current AbstractImplCtx object.</p>
 <p>Calling <code class="docutils literal notranslate"><span class="pre">get_ctx()</span></code> is only valid inside of an fake impl
 (see <a class="reference internal" href="#torch.library.register_fake" title="torch.library.register_fake"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.register_fake()</span></code></a> for more usage details.</p>
@@ -1352,7 +1352,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_torch_dispatch">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_torch_dispatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_dispatch_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_torch_dispatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_torch_dispatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_torch_dispatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_dispatch_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_torch_dispatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_torch_dispatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a torch_dispatch rule for the given operator and <code class="docutils literal notranslate"><span class="pre">torch_dispatch_class</span></code>.</p>
 <p>This allows for open registration to specify the behavior between the operator
 and the <code class="docutils literal notranslate"><span class="pre">torch_dispatch_class</span></code> without needing to modify the <code class="docutils literal notranslate"><span class="pre">torch_dispatch_class</span></code>
@@ -1396,7 +1396,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.infer_schema">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">infer_schema</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prototype_function</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_library/infer_schema.py#L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.infer_schema" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">infer_schema</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prototype_function</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/infer_schema.py#L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.infer_schema" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parses the schema of a given function with type hints. The schema is inferred from the
 function’s type hints, and can be used to define a new operator.</p>
 <p>We make the following assumptions:</p>
@@ -1447,7 +1447,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch._library.custom_ops.CustomOpDef">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch._library.custom_ops.</span></span><span class="sig-name descname"><span class="pre">CustomOpDef</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_library/custom_ops.html#CustomOpDef"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_library/custom_ops.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._library.custom_ops.CustomOpDef" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch._library.custom_ops.</span></span><span class="sig-name descname"><span class="pre">CustomOpDef</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_library/custom_ops.html#CustomOpDef"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/custom_ops.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._library.custom_ops.CustomOpDef" title="Permalink to this definition">¶</a></dt>
 <dd><p>CustomOpDef is a wrapper around a function that turns it into a custom op.</p>
 <p>It has various methods for registering additional behavior for this
 custom op.</p>
@@ -1457,7 +1457,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch._library.custom_ops.CustomOpDef.set_kernel_enabled">
-<span class="sig-name descname"><span class="pre">set_kernel_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_library/custom_ops.html#CustomOpDef.set_kernel_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_library/custom_ops.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._library.custom_ops.CustomOpDef.set_kernel_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_kernel_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_library/custom_ops.html#CustomOpDef.set_kernel_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/custom_ops.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._library.custom_ops.CustomOpDef.set_kernel_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable or re-enable an already registered kernel for this custom operator.</p>
 <p>If the kernel is already disabled/enabled, this is a no-op.</p>
 <div class="admonition note">
@@ -1513,7 +1513,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 <p>A tutorial that walks you through some examples on how to use this API is available on <a class="reference external" href="https://colab.research.google.com/drive/1RRhSfk7So3Cn02itzLWE9K4Fam-8U011?usp=sharing">Google Colab</a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.library.Library">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">Library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">Library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library" title="Permalink to this definition">¶</a></dt>
 <dd><p>A class to create libraries that can be used to register new operators or
 override operators in existing libraries from Python.
 A user can optionally pass in a dispatch keyname if they only want to register
@@ -1534,7 +1534,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.library.Library.define">
-<span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alias_analysis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.define" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alias_analysis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.define" title="Permalink to this definition">¶</a></dt>
 <dd><p>Defines a new operator and its semantics in the ns namespace.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1563,7 +1563,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.library.Library.fallback">
-<span class="sig-name descname"><span class="pre">fallback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_keyset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.fallback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.fallback" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fallback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_keyset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.fallback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.fallback" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers the function implementation as the fallback for the given key.</p>
 <p>This function only works for a library with global namespace (“_”).</p>
 <dl class="field-list simple">
@@ -1592,7 +1592,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.library.Library.impl">
-<span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_keyset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.impl" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_keyset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.impl" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers the function implementation for an operator defined in the library.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1622,13 +1622,13 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.fallthrough_kernel">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">fallthrough_kernel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#fallthrough_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.fallthrough_kernel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">fallthrough_kernel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#fallthrough_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.fallthrough_kernel" title="Permalink to this definition">¶</a></dt>
 <dd><p>A dummy function to pass to <code class="docutils literal notranslate"><span class="pre">Library.impl</span></code> in order to register a fallthrough.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.define">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.define" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.define" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alias_analysis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span></dt>
 <dd><p>Defines a new operator.</p>
@@ -1685,7 +1685,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.impl">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/library.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">types</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="pre">None</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.library.Library" title="torch.library.Library"><span class="pre">Library</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
 <dt class="sig sig-object py">
diff --git a/main/linalg.html b/main/linalg.html
index 22ea4234986..9048b22a3f7 100644
--- a/main/linalg.html
+++ b/main/linalg.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/logging.html b/main/logging.html
index 44633d250a6..b772d8484c2 100644
--- a/main/logging.html
+++ b/main/logging.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/masked.html b/main/masked.html
index facd70cb545..ed84d02604a 100644
--- a/main/masked.html
+++ b/main/masked.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/meta.html b/main/meta.html
index 6358d5c5455..1f083d66a1c 100644
--- a/main/meta.html
+++ b/main/meta.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/miscellaneous_environment_variables.html b/main/miscellaneous_environment_variables.html
index 1180b7023e1..4a27734d5b0 100644
--- a/main/miscellaneous_environment_variables.html
+++ b/main/miscellaneous_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/mobile_optimizer.html b/main/mobile_optimizer.html
index 062978abdfe..fd067a773c5 100644
--- a/main/mobile_optimizer.html
+++ b/main/mobile_optimizer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -636,7 +636,7 @@ <h1>torch.utils.mobile_optimizer<a class="headerlink" href="#torch-utils-mobile-
 <p><code class="docutils literal notranslate"><span class="pre">optimize_for_mobile</span></code> will also invoke freeze_module pass which only preserves <code class="docutils literal notranslate"><span class="pre">forward</span></code> method. If you have other method to that needed to be preserved, add them into the preserved method list and pass into the method.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.mobile_optimizer.optimize_for_mobile">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.mobile_optimizer.</span></span><span class="sig-name descname"><span class="pre">optimize_for_mobile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">script_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_blocklist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserved_methods</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CPU'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/mobile_optimizer.html#optimize_for_mobile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/mobile_optimizer.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.mobile_optimizer.optimize_for_mobile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.mobile_optimizer.</span></span><span class="sig-name descname"><span class="pre">optimize_for_mobile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">script_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_blocklist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserved_methods</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CPU'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/mobile_optimizer.html#optimize_for_mobile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/mobile_optimizer.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.mobile_optimizer.optimize_for_mobile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Optimize a torch script module for mobile deployment.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/model_zoo.html b/main/model_zoo.html
index fa4c93f81a1..cfb5f3e76a9 100644
--- a/main/model_zoo.html
+++ b/main/model_zoo.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -611,7 +611,7 @@ <h1>torch.utils.model_zoo<a class="headerlink" href="#torch-utils-model-zoo" tit
 <p>Moved to <cite>torch.hub</cite>.</p>
 <span class="target" id="module-torch.utils.model_zoo"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.model_zoo.load_url">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.model_zoo.</span></span><span class="sig-name descname"><span class="pre">load_url</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/hub.py#L803"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.model_zoo.load_url" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.model_zoo.</span></span><span class="sig-name descname"><span class="pre">load_url</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L803"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.model_zoo.load_url" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads the Torch serialized object at the given URL.</p>
 <p>If downloaded file is a zip file, it will be automatically
 decompressed.</p>
diff --git a/main/module_tracker.html b/main/module_tracker.html
index da49e2780e8..d3c405bf86f 100644
--- a/main/module_tracker.html
+++ b/main/module_tracker.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 It can be used within other tracking tools to be able to easily associate measured quantities to user-friendly names. This is used in particular in the FlopCounterMode today.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.module_tracker.ModuleTracker">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.module_tracker.</span></span><span class="sig-name descname"><span class="pre">ModuleTracker</span></span><a class="reference internal" href="_modules/torch/utils/module_tracker.html#ModuleTracker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/module_tracker.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.module_tracker.ModuleTracker" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.module_tracker.</span></span><span class="sig-name descname"><span class="pre">ModuleTracker</span></span><a class="reference internal" href="_modules/torch/utils/module_tracker.html#ModuleTracker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/module_tracker.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.module_tracker.ModuleTracker" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">ModuleTracker</span></code> is a context manager that tracks the nn.Module hierarchy during execution
 so that other system can query which Module is currently being executed (or its backward is being
 executed).</p>
diff --git a/main/monitor.html b/main/monitor.html
index ebf825102dc..7e6b03e9ddf 100644
--- a/main/monitor.html
+++ b/main/monitor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -785,7 +785,7 @@ <h1>torch.monitor<a class="headerlink" href="#torch-monitor" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.monitor.TensorboardEventHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.monitor.</span></span><span class="sig-name descname"><span class="pre">TensorboardEventHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/monitor/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.monitor.</span></span><span class="sig-name descname"><span class="pre">TensorboardEventHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/monitor/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>TensorboardEventHandler is an event handler that will write known events to
 the provided SummaryWriter.</p>
 <p>This currently only supports <code class="docutils literal notranslate"><span class="pre">torch.monitor.Stat</span></code> events which are logged
@@ -801,7 +801,7 @@ <h1>torch.monitor<a class="headerlink" href="#torch-monitor" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.monitor.TensorboardEventHandler.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/monitor/__init__.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/monitor/__init__.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs the <code class="docutils literal notranslate"><span class="pre">TensorboardEventHandler</span></code>.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/mps.html b/main/mps.html
index 7a59a5c3229..f4ab07221ba 100644
--- a/main/mps.html
+++ b/main/mps.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/mps_environment_variables.html b/main/mps_environment_variables.html
index 09bdc2b9ebb..1e554d07c80 100644
--- a/main/mps_environment_variables.html
+++ b/main/mps_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/mtia.html b/main/mtia.html
index e554918b44a..711a71d3c60 100644
--- a/main/mtia.html
+++ b/main/mtia.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/mtia.memory.html b/main/mtia.memory.html
index ba801710120..3453d99df72 100644
--- a/main/mtia.memory.html
+++ b/main/mtia.memory.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/multiprocessing.html b/main/multiprocessing.html
index e9e64b6c46a..492bbd1e627 100644
--- a/main/multiprocessing.html
+++ b/main/multiprocessing.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -629,19 +629,19 @@
 <h2>Strategy management<a class="headerlink" href="#strategy-management" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.multiprocessing.get_all_sharing_strategies">
-<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">get_all_sharing_strategies</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_all_sharing_strategies"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/multiprocessing/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.get_all_sharing_strategies" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">get_all_sharing_strategies</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_all_sharing_strategies"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.get_all_sharing_strategies" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a set of sharing strategies supported on a current system.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.multiprocessing.get_sharing_strategy">
-<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">get_sharing_strategy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_sharing_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/multiprocessing/__init__.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.get_sharing_strategy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">get_sharing_strategy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_sharing_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/__init__.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.get_sharing_strategy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current strategy for sharing CPU tensors.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.multiprocessing.set_sharing_strategy">
-<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">set_sharing_strategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_strategy</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#set_sharing_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/multiprocessing/__init__.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.set_sharing_strategy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">set_sharing_strategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_strategy</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#set_sharing_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/__init__.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.set_sharing_strategy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the strategy for sharing CPU tensors.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -778,7 +778,7 @@ <h2>Spawning subprocesses<a class="headerlink" href="#spawning-subprocesses" tit
 terminate processes upon detecting an error in one of them.</p>
 <span class="target" id="module-torch.multiprocessing.spawn"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.multiprocessing.spawn.spawn">
-<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.spawn.</span></span><span class="sig-name descname"><span class="pre">spawn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprocs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">join</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing/spawn.html#spawn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/multiprocessing/spawn.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.spawn.spawn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.spawn.</span></span><span class="sig-name descname"><span class="pre">spawn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprocs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">join</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing/spawn.html#spawn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/spawn.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.spawn.spawn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Spawns <code class="docutils literal notranslate"><span class="pre">nprocs</span></code> processes that run <code class="docutils literal notranslate"><span class="pre">fn</span></code> with <code class="docutils literal notranslate"><span class="pre">args</span></code>.</p>
 <p>If one of the processes exits with a non-zero exit status, the
 remaining processes are killed and an exception is raised with the
@@ -815,11 +815,11 @@ <h2>Spawning subprocesses<a class="headerlink" href="#spawning-subprocesses" tit
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.multiprocessing.SpawnContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">SpawnContext</span></span><a class="reference internal" href="_modules/torch/multiprocessing/spawn.html#SpawnContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/multiprocessing/spawn.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.SpawnContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">SpawnContext</span></span><a class="reference internal" href="_modules/torch/multiprocessing/spawn.html#SpawnContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/spawn.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.SpawnContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returned by <a class="reference internal" href="#module-torch.multiprocessing.spawn" title="torch.multiprocessing.spawn"><code class="xref py py-func docutils literal notranslate"><span class="pre">spawn()</span></code></a> when called with <code class="docutils literal notranslate"><span class="pre">join=False</span></code>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.multiprocessing.SpawnContext.join">
-<span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grace_period</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/multiprocessing/spawn.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.SpawnContext.join" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grace_period</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/spawn.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.SpawnContext.join" title="Permalink to this definition">¶</a></dt>
 <dd><p>Join one or more processes within spawn context.</p>
 <p>Attempt to join one or more processes in this spawn context.
 If one of them exited with a non-zero exit status, this function
diff --git a/main/name_inference.html b/main/name_inference.html
index 809acf0bcf9..1aa2d07d227 100644
--- a/main/name_inference.html
+++ b/main/name_inference.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/named_tensor.html b/main/named_tensor.html
index e8aef17f21f..169f08cde73 100644
--- a/main/named_tensor.html
+++ b/main/named_tensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -875,7 +875,7 @@ <h2>Named tensor API reference<a class="headerlink" href="#named-tensor-api-refe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.rename">
-<span class="sig-name descname"><span class="pre">rename</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">rename_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.rename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1440"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.rename" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rename</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">rename_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.rename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1440"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.rename" title="Permalink to this definition">¶</a></dt>
 <dd><p>Renames dimension names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>There are two main usages:</p>
 <p><code class="docutils literal notranslate"><span class="pre">self.rename(**rename_map)</span></code> returns a view on tensor that has dims
@@ -908,13 +908,13 @@ <h2>Named tensor API reference<a class="headerlink" href="#named-tensor-api-refe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.rename_">
-<span class="sig-name descname"><span class="pre">rename_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">rename_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.rename_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.rename_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rename_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">rename_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.rename_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.rename_" title="Permalink to this definition">¶</a></dt>
 <dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.rename" title="torch.Tensor.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rename()</span></code></a>.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.refine_names">
-<span class="sig-name descname"><span class="pre">refine_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.refine_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.refine_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">refine_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.refine_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.refine_names" title="Permalink to this definition">¶</a></dt>
 <dd><p>Refines the dimension names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> according to <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a>.</p>
 <p>Refining is a special case of renaming that “lifts” unnamed dimensions.
 A <code class="docutils literal notranslate"><span class="pre">None</span></code> dim can be refined to have any name; a named dim can only be
@@ -996,7 +996,7 @@ <h2>Named tensor API reference<a class="headerlink" href="#named-tensor-api-refe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.align_to">
-<span class="sig-name descname"><span class="pre">align_to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.align_to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/_tensor.py#L1358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.align_to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">align_to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.align_to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.align_to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Permutes the dimensions of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor to match the order
 specified in <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a>, adding size-one dims for any new names.</p>
 <p>All of the dims of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> must be named in order to use this method.
diff --git a/main/nested.html b/main/nested.html
index 2ea2190ef8e..99e3aa4b934 100644
--- a/main/nested.html
+++ b/main/nested.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1045,7 +1045,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 <span id="construction-and-conversion"></span><h2>Detailed Docs for Construction and Conversion Functions<a class="headerlink" href="#detailed-docs-for-construction-and-conversion-functions" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.nested_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#nested_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nested/__init__.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.nested_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#nested_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.nested_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a nested tensor with no autograd history (also known as a “leaf tensor”, see
 <a class="reference internal" href="notes/autograd.html#autograd-mechanics"><span class="std std-ref">Autograd mechanics</span></a>) from <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor_list</span></code> a list of tensors.</p>
 <dl class="field-list simple">
@@ -1085,7 +1085,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.nested_tensor_from_jagged">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">nested_tensor_from_jagged</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jagged_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_seqlen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_seqlen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#nested_tensor_from_jagged"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nested/__init__.py#L359"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.nested_tensor_from_jagged" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">nested_tensor_from_jagged</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jagged_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_seqlen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_seqlen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#nested_tensor_from_jagged"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L359"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.nested_tensor_from_jagged" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a jagged layout nested tensor from the given jagged components. The jagged layout
 consists of a required values buffer with the jagged dimension packed into a single dimension.
 The offsets / lengths metadata determines how this dimension is split into batch elements
@@ -1161,7 +1161,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.as_nested_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">as_nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#as_nested_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nested/__init__.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.as_nested_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">as_nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#as_nested_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.as_nested_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a nested tensor preserving autograd history from a tensor or a list / tuple of
 tensors.</p>
 <p>If a nested tensor is passed, it will be returned directly unless the device / dtype / layout
@@ -1266,7 +1266,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.masked_select">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">masked_select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#masked_select"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nested/__init__.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.masked_select" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">masked_select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#masked_select"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.masked_select" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a nested tensor given a strided tensor input and a strided mask, the resulting jagged layout nested tensor
 will have values retain values where the mask is equal to True. The dimensionality of the mask is preserved and is
 represented with the offsets, this is unlike <a class="reference internal" href="#torch.nested.masked_select" title="torch.nested.masked_select"><code class="xref py py-func docutils literal notranslate"><span class="pre">masked_select()</span></code></a> where the output is collapsed to a 1D tensor.</p>
@@ -1302,7 +1302,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.narrow">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">narrow</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#narrow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nested/__init__.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.narrow" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">narrow</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#narrow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.narrow" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a nested tensor (which might be a view) from <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code>, a strided tensor. This follows
 similar semantics to torch.Tensor.narrow, where in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>-th dimension the new nested tensor
 shows only the elements in the interval <cite>[start, start+length)</cite>. As nested representations
diff --git a/main/nn.attention.bias.html b/main/nn.attention.bias.html
index 6055ae6931a..ce9db5cd597 100644
--- a/main/nn.attention.bias.html
+++ b/main/nn.attention.bias.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.attention.experimental.html b/main/nn.attention.experimental.html
index 5ce3708a317..4249f10d517 100644
--- a/main/nn.attention.experimental.html
+++ b/main/nn.attention.experimental.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.attention.flex_attention.html b/main/nn.attention.flex_attention.html
index e3d60f7a4d8..bac7b319480 100644
--- a/main/nn.attention.flex_attention.html
+++ b/main/nn.attention.flex_attention.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <span id="torch-nn-attention-flex-attention"></span><h1>torch.nn.attention.flex_attention<a class="headerlink" href="#module-torch.nn.attention.flex_attention" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.flex_attention">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">flex_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_mod</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_gqa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_lse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#flex_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L1161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.flex_attention" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">flex_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_mod</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_gqa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_lse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#flex_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L1161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.flex_attention" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function implements scaled dot product attention with an arbitrary attention score modification function.</p>
 <p>This function computes the scaled dot product attention between query, key, and value tensors with a user-defined
 attention score modification function. The attention score modification function will be applied after the attention
@@ -691,7 +691,7 @@
 <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.create_block_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_block_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">KV_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_block_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_block_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_block_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">KV_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_block_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_block_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function creates a block mask tuple from a mask_mod function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -733,7 +733,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.create_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">KV_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L777"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">KV_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L777"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function creates a mask tensor from a mod_fn function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -757,7 +757,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.create_nested_block_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_nested_block_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_nt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_nt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_nested_block_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L998"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_nested_block_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_nested_block_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_nt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_nt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_nested_block_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L998"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_nested_block_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function creates a nested tensor compatible block mask tuple from a mask_mod
 function. The returned BlockMask will be on the device specified by the input nested tensor.</p>
 <dl class="field-list simple">
@@ -821,7 +821,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.and_masks">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">and_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">mask_mods</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#and_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L721"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.and_masks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">and_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">mask_mods</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#and_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L721"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.and_masks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a mask_mod that’s the intersection of provided mask_mods</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -832,7 +832,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.or_masks">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">or_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">mask_mods</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#or_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L707"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.or_masks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">or_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">mask_mods</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#or_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L707"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.or_masks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a mask_mod that’s the union of provided mask_mods</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -843,7 +843,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.noop_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">noop_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_kv</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#noop_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.noop_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">noop_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_kv</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#noop_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.noop_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a noop mask_mod</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -857,7 +857,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 <h2>BlockMask<a class="headerlink" href="#blockmask" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">BlockMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seq_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_q_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_q_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">BlockMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seq_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_q_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_q_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask" title="Permalink to this definition">¶</a></dt>
 <dd><p>BlockMask is our format for representing a block-sparse attention mask.
 It is somewhat of a cross in-between BCSR and a non-sparse format.</p>
 <div class="section" id="basics">
@@ -910,7 +910,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.as_tuple">
-<span class="sig-name descname"><span class="pre">as_tuple</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.as_tuple"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.as_tuple" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">as_tuple</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.as_tuple"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.as_tuple" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a tuple of the attributes of the BlockMask.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -921,7 +921,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.from_kv_blocks">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_kv_blocks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_num_blocks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.from_kv_blocks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.from_kv_blocks" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_kv_blocks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_num_blocks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.from_kv_blocks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.from_kv_blocks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a BlockMask instance from key-value block information.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -986,7 +986,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.numel">
-<span class="sig-name descname"><span class="pre">numel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.numel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.numel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">numel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.numel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.numel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the number of elements (not accounting for sparsity) in the mask.</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.sparsity">
-<span class="sig-name descname"><span class="pre">sparsity</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.sparsity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.sparsity" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sparsity</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.sparsity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.sparsity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the percentage of blocks that are sparse (i.e. not computed)</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1023,7 +1023,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Moves the BlockMask to the specified device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1049,7 +1049,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.to_dense">
-<span class="sig-name descname"><span class="pre">to_dense</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to_dense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L550"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to_dense" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dense</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to_dense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L550"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to_dense" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a dense block that is equivalent to the block mask.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1060,7 +1060,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.to_string">
-<span class="sig-name descname"><span class="pre">to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grid_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(20,</span> <span class="pre">20)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/attention/flex_attention.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to_string" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grid_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(20,</span> <span class="pre">20)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to_string" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a string representation of the block mask. Quite nifty.</p>
 <p>If grid_size is None, prints out an uncompressed version. Warning, it can be quite big!</p>
 </dd></dl>
diff --git a/main/nn.attention.html b/main/nn.attention.html
index 4a3924e62f9..dc0efe72a83 100644
--- a/main/nn.attention.html
+++ b/main/nn.attention.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.functional.html b/main/nn.functional.html
index ea4e4ebb085..45c99bef2ef 100644
--- a/main/nn.functional.html
+++ b/main/nn.functional.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.html b/main/nn.html
index ed554625e85..2788af4079e 100644
--- a/main/nn.html
+++ b/main/nn.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.init.html b/main/nn.init.html
index bcdbabe2697..cc8dd705273 100644
--- a/main/nn.init.html
+++ b/main/nn.init.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.calculate_gain">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">calculate_gain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#calculate_gain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.calculate_gain" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">calculate_gain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#calculate_gain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.calculate_gain" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the recommended gain value for the given nonlinearity function.</p>
 <p>The values are as follows:</p>
 <table class="docutils colwidths-auto align-default">
@@ -694,7 +694,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.uniform_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.uniform_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.uniform_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with values drawn from the uniform distribution.</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">U</mi><mo stretchy="false">(</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{U}(a, b)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.09931em;">U</span><span class="mopen">(</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">b</span><span class="mclose">)</span></span></span></span></span>.</p>
 <dl class="field-list simple">
@@ -719,7 +719,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.normal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.normal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.normal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with values drawn from the normal distribution.</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mtext>mean</mtext><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(\text{mean}, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord text"><span class="mord">mean</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>.</p>
 <dl class="field-list simple">
@@ -744,7 +744,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.constant_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">constant_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#constant_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.constant_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">constant_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#constant_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.constant_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with the value <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>val</mtext></mrow><annotation encoding="application/x-tex">\text{val}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">val</span></span></span></span></span></span>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -766,7 +766,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.ones_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">ones_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#ones_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.ones_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">ones_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#ones_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.ones_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with the scalar value <cite>1</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -785,7 +785,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.zeros_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">zeros_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#zeros_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.zeros_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">zeros_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#zeros_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.zeros_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with the scalar value <cite>0</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -804,7 +804,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.eye_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">eye_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#eye_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.eye_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">eye_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#eye_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.eye_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the 2-dimensional input <cite>Tensor</cite> with the identity matrix.</p>
 <p>Preserves the identity of the inputs in <cite>Linear</cite> layers, where as
 many inputs are preserved as possible.</p>
@@ -822,7 +822,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.dirac_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">dirac_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#dirac_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.dirac_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">dirac_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#dirac_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.dirac_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the {3, 4, 5}-dimensional input <cite>Tensor</cite> with the Dirac delta function.</p>
 <p>Preserves the identity of the inputs in <cite>Convolutional</cite>
 layers, where as many input channels are preserved as possible. In case
@@ -846,7 +846,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.xavier_uniform_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#xavier_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_uniform_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#xavier_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_uniform_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with values using a Xavier uniform distribution.</p>
 <p>The method is described in <cite>Understanding the difficulty of training
 deep feedforward neural networks</cite> - Glorot, X. &amp; Bengio, Y. (2010).
@@ -893,7 +893,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.xavier_normal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#xavier_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_normal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#xavier_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_normal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with values using a Xavier normal distribution.</p>
 <p>The method is described in <cite>Understanding the difficulty of training deep feedforward
 neural networks</cite> - Glorot, X. &amp; Bengio, Y. (2010). The resulting tensor
@@ -939,7 +939,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.kaiming_uniform_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#kaiming_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_uniform_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#kaiming_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_uniform_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with values using a Kaiming uniform distribution.</p>
 <p>The method is described in <cite>Delving deep into rectifiers: Surpassing
 human-level performance on ImageNet classification</cite> - He, K. et al. (2015).
@@ -990,7 +990,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.kaiming_normal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#kaiming_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_normal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#kaiming_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_normal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with values using a Kaiming normal distribution.</p>
 <p>The method is described in <cite>Delving deep into rectifiers: Surpassing
 human-level performance on ImageNet classification</cite> - He, K. et al. (2015).
@@ -1044,7 +1044,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.trunc_normal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">trunc_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#trunc_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.trunc_normal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">trunc_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#trunc_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.trunc_normal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with values drawn from a truncated normal distribution.</p>
 <p>The values are effectively drawn from the
 normal distribution <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mtext>mean</mtext><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(\text{mean}, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord text"><span class="mord">mean</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>
@@ -1075,7 +1075,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.orthogonal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">orthogonal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#orthogonal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.orthogonal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">orthogonal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#orthogonal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.orthogonal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with a (semi) orthogonal matrix.</p>
 <p>Described in <cite>Exact solutions to the nonlinear dynamics of learning in deep
 linear neural networks</cite> - Saxe, A. et al. (2013). The input tensor must have
@@ -1099,7 +1099,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.sparse_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">sparse_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparsity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#sparse_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/nn/init.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.sparse_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">sparse_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparsity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#sparse_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.sparse_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the 2D input <cite>Tensor</cite> as a sparse matrix.</p>
 <p>The non-zero elements will be drawn from the normal distribution
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mn>0.01</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(0, 0.01)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">0.01</span><span class="mclose">)</span></span></span></span></span>, as described in <cite>Deep learning via
diff --git a/main/notes/amp_examples.html b/main/notes/amp_examples.html
index df943c216b9..59dc90c3c3d 100644
--- a/main/notes/amp_examples.html
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index 90dbccf38ac..05942d45b8c 100644
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/notes/cuda.html b/main/notes/cuda.html
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@@ -1028,7 +1028,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 <p>The following utility functions are related to serialization:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.register_package">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">register_package</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">priority</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tagger</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deserializer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#register_package"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.register_package" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">register_package</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">priority</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tagger</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deserializer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#register_package"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.register_package" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers callables for tagging and deserializing storage objects with an associated priority.
 Tagging associates a device with a storage object at save time while deserializing moves a
 storage object to an appropriate device at load time. <code class="xref py py-attr docutils literal notranslate"><span class="pre">tagger</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">deserializer</span></code>
@@ -1069,7 +1069,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_crc32_options">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_crc32_options</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_crc32_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_crc32_options" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_crc32_options</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_crc32_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_crc32_options" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get whether <a class="reference internal" href="../generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> computes and writes crc32 for each record.</p>
 <p>Defaults to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <dl class="field-list simple">
@@ -1081,7 +1081,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.set_crc32_options">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_crc32_options</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">compute_crc32</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_crc32_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_crc32_options" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_crc32_options</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">compute_crc32</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_crc32_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_crc32_options" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether <a class="reference internal" href="../generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> computes and writes crc32 for each record.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1098,7 +1098,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_default_load_endianness">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_default_load_endianness</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_default_load_endianness" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_default_load_endianness</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_default_load_endianness" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get fallback byte order for loading files</p>
 <p>If byteorder mark is not present in saved checkpoint,
 this byte order is used as fallback.
@@ -1115,7 +1115,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.set_default_load_endianness">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_default_load_endianness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">endianness</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_default_load_endianness" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_default_load_endianness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">endianness</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_default_load_endianness" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set fallback byte order for loading files</p>
 <p>If byteorder mark is not present in saved checkpoint,
 this byte order is used as fallback.
@@ -1129,7 +1129,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_default_mmap_options">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_default_mmap_options</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_default_mmap_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_default_mmap_options" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_default_mmap_options</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_default_mmap_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_default_mmap_options" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get default mmap options for <a class="reference internal" href="../generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> with <code class="docutils literal notranslate"><span class="pre">mmap=True</span></code>.</p>
 <p>Defaults to <code class="docutils literal notranslate"><span class="pre">mmap.MAP_PRIVATE</span></code>.</p>
 <dl class="field-list simple">
@@ -1144,7 +1144,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.set_default_mmap_options">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_default_mmap_options</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flags</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_default_mmap_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_default_mmap_options" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_default_mmap_options</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flags</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_default_mmap_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_default_mmap_options" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager or function to set default mmap options for <a class="reference internal" href="../generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> with <code class="docutils literal notranslate"><span class="pre">mmap=True</span></code> to flags.</p>
 <p>For now, only either <code class="docutils literal notranslate"><span class="pre">mmap.MAP_PRIVATE</span></code> or <code class="docutils literal notranslate"><span class="pre">mmap.MAP_SHARED</span></code> are supported.
 Please open an issue if you need any other option to be added here.</p>
@@ -1161,7 +1161,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.add_safe_globals">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">add_safe_globals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">safe_globals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#add_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.add_safe_globals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">add_safe_globals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">safe_globals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#add_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.add_safe_globals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Marks the given globals as safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code> load. For example, functions
 added to this list can be called during unpickling, classes could be instantiated
 and have state set.</p>
@@ -1196,7 +1196,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.clear_safe_globals">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">clear_safe_globals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#clear_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L253"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.clear_safe_globals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">clear_safe_globals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#clear_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L253"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.clear_safe_globals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clears the list of globals that are safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code> load.</p>
 <dl class="field-list simple">
 </dl>
@@ -1204,7 +1204,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_safe_globals">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_safe_globals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_safe_globals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_safe_globals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_safe_globals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the list of user-added globals that are safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code> load.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1215,7 +1215,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_unsafe_globals_in_checkpoint">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_unsafe_globals_in_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_unsafe_globals_in_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L328"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_unsafe_globals_in_checkpoint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_unsafe_globals_in_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_unsafe_globals_in_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L328"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_unsafe_globals_in_checkpoint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of strings of functions/classes in a <code class="docutils literal notranslate"><span class="pre">torch.save</span></code> object that are not safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code>.</p>
 <p>For a given function or class <code class="docutils literal notranslate"><span class="pre">f</span></code>, the corresponding string will be of the form
 <code class="docutils literal notranslate"><span class="pre">{f.__module__}.{f.__name__}</span></code>.</p>
@@ -1243,7 +1243,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.serialization.safe_globals">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">safe_globals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">safe_globals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L303"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.safe_globals" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">safe_globals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">safe_globals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L303"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.safe_globals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that adds certain globals as safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code> load.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1271,7 +1271,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.serialization.skip_data">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">skip_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">materialize_fake_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#skip_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/serialization.py#L370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.skip_data" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">skip_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">materialize_fake_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#skip_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.skip_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that skips writing storage bytes for <code class="docutils literal notranslate"><span class="pre">torch.save</span></code> calls.</p>
 <p>Storages will still be saved, but the space that their bytes would usually be written to
 will be empty space. The storage bytes can then be populated in a separate pass.</p>
diff --git a/main/notes/windows.html b/main/notes/windows.html
index c3268e5b394..c44bb491840 100644
--- a/main/notes/windows.html
+++ b/main/notes/windows.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/objects.inv b/main/objects.inv
index 0d8dcf24825..b46b7db2adf 100644
Binary files a/main/objects.inv and b/main/objects.inv differ
diff --git a/main/onnx.html b/main/onnx.html
index fa1edd53ec3..b3cdca49124 100644
--- a/main/onnx.html
+++ b/main/onnx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/onnx_dynamo.html b/main/onnx_dynamo.html
index c8662efceda..ffeb06471a0 100644
--- a/main/onnx_dynamo.html
+++ b/main/onnx_dynamo.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -721,7 +721,7 @@ <h2><a class="toc-backref" href="#id5">When the conversion fails</a><a class="he
 <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.dynamo_export">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">dynamo_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">model_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#dynamo_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/__init__.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.dynamo_export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">dynamo_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">model_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#dynamo_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/__init__.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.dynamo_export" title="Permalink to this definition">¶</a></dt>
 <dd><p>Export a torch.nn.Module to an ONNX graph.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -778,7 +778,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.apply_weights">
-<span class="sig-name descname"><span class="pre">apply_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.apply_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/exporter/_onnx_program.py#L192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.apply_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.apply_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.apply_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the weights from the specified state dict to the ONNX model.</p>
 <p>Use this method to replace FakeTensors or other weights.</p>
 <dl class="field-list simple">
@@ -790,7 +790,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.initialize_inference_session">
-<span class="sig-name descname"><span class="pre">initialize_inference_session</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initializer=&lt;function</span> <span class="pre">_ort_session_initializer&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.initialize_inference_session"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/exporter/_onnx_program.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.initialize_inference_session" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">initialize_inference_session</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initializer=&lt;function</span> <span class="pre">_ort_session_initializer&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.initialize_inference_session"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.initialize_inference_session" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize the ONNX Runtime inference session.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -809,7 +809,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.optimize">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.optimize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/exporter/_onnx_program.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.optimize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.optimize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.optimize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Optimize the ONNX model.</p>
 <p>This method optimizes the ONNX model by performing constant folding and
 eliminating redundancies in the graph. The optimization is done in-place.</p>
@@ -819,7 +819,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.release">
-<span class="sig-name descname"><span class="pre">release</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.release"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/exporter/_onnx_program.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.release" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">release</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.release"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.release" title="Permalink to this definition">¶</a></dt>
 <dd><p>Release the inference session.</p>
 <p>You may call this method to release the resources used by the inference session.</p>
 <dl class="field-list simple">
@@ -828,7 +828,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.save">
-<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_initializers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/exporter/_onnx_program.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_initializers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save the ONNX model to the specified destination.</p>
 <p>When <code class="docutils literal notranslate"><span class="pre">external_data</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code> or the model is larger than 2GB,
 the weights are saved as external data in a separate file.</p>
@@ -887,7 +887,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.enable_fake_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">enable_fake_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/_exporter_legacy.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.enable_fake_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">enable_fake_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/_exporter_legacy.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.enable_fake_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable fake mode for the duration of the context.</p>
 <p>Internally it instantiates a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensorMode</span></code> context manager
 that converts user input and model parameters into <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensor</span></code>.</p>
@@ -962,7 +962,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.OnnxRegistry.get_op_functions">
-<span class="sig-name descname"><span class="pre">get_op_functions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.get_op_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/_exporter_legacy.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.get_op_functions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_op_functions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.get_op_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/_exporter_legacy.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.get_op_functions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of ONNXFunctions for the given op: torch.ops.&lt;namespace&gt;.&lt;op_name&gt;.&lt;overload&gt;.</p>
 <p>The list is ordered by the time of registration. The custom operators should be
 in the second half of the list.</p>
@@ -987,7 +987,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.OnnxRegistry.is_registered_op">
-<span class="sig-name descname"><span class="pre">is_registered_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.is_registered_op"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/_exporter_legacy.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.is_registered_op" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_registered_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.is_registered_op"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/_exporter_legacy.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.is_registered_op" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether the given op is registered: torch.ops.&lt;namespace&gt;.&lt;op_name&gt;.&lt;overload&gt;.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1015,7 +1015,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.OnnxRegistry.register_op">
-<span class="sig-name descname"><span class="pre">register_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.register_op"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/_exporter_legacy.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.register_op" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.register_op"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/_exporter_legacy.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.register_op" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a custom operator: torch.ops.&lt;namespace&gt;.&lt;op_name&gt;.&lt;overload&gt;.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/onnx_dynamo_memory_usage.html b/main/onnx_dynamo_memory_usage.html
index 4b7ec6c1c22..49c62caf0f3 100644
--- a/main/onnx_dynamo_memory_usage.html
+++ b/main/onnx_dynamo_memory_usage.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/onnx_dynamo_onnxruntime_backend.html b/main/onnx_dynamo_onnxruntime_backend.html
index 7a445a8e262..80840e48b54 100644
--- a/main/onnx_dynamo_onnxruntime_backend.html
+++ b/main/onnx_dynamo_onnxruntime_backend.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -617,7 +617,7 @@ <h1>ONNX Backend for TorchDynamo<a class="headerlink" href="#onnx-backend-for-to
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.is_onnxrt_backend_supported">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_onnxrt_backend_supported</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/_internal/onnxruntime.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.is_onnxrt_backend_supported" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_onnxrt_backend_supported</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/onnxruntime.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.is_onnxrt_backend_supported" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if ONNX Runtime dependencies are installed and usable
 to support TorchDynamo backend integration; <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
 <p>Example:</p>
diff --git a/main/onnx_torchscript.html b/main/onnx_torchscript.html
index de10f5e71d6..438cbe4746e 100644
--- a/main/onnx_torchscript.html
+++ b/main/onnx_torchscript.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1258,7 +1258,7 @@ <h2><a class="toc-backref" href="#id27">Frequently Asked Questions</a><a class="
 <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href="#functions" title="Permalink to this heading">¶</a></h3>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.export">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args=()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_params=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_axes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_translation_table=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">report=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verify=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dump_exported_program=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">artifacts_dir='.'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training=&lt;TrainingMode.EVAL:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator_export_type=&lt;OperatorExportTypes.ONNX:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_constant_folding=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_opsets=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_modules_as_functions=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autograd_inlining=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**_</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/__init__.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args=()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_params=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_axes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_translation_table=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">report=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verify=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dump_exported_program=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">artifacts_dir='.'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training=&lt;TrainingMode.EVAL:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator_export_type=&lt;OperatorExportTypes.ONNX:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_constant_folding=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_opsets=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_modules_as_functions=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autograd_inlining=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**_</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/__init__.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.export" title="Permalink to this definition">¶</a></dt>
 <dd><p>Exports a model into ONNX format.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1452,7 +1452,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.register_custom_op_symbolic">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">register_custom_op_symbolic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbolic_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#register_custom_op_symbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/utils.py#L1760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.register_custom_op_symbolic" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">register_custom_op_symbolic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbolic_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#register_custom_op_symbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/utils.py#L1760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.register_custom_op_symbolic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a symbolic function for a custom operator.</p>
 <p>When the user registers symbolic for custom/contrib ops,
 it is highly recommended to add shape inference for that operator via setType API,
@@ -1475,7 +1475,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.unregister_custom_op_symbolic">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">unregister_custom_op_symbolic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbolic_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#unregister_custom_op_symbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/utils.py#L1790"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.unregister_custom_op_symbolic" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">unregister_custom_op_symbolic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbolic_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#unregister_custom_op_symbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/utils.py#L1790"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.unregister_custom_op_symbolic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unregisters <code class="docutils literal notranslate"><span class="pre">symbolic_name</span></code>.</p>
 <p>See “Custom Operators” in the module documentation for an example usage.</p>
 <dl class="field-list simple">
@@ -1491,7 +1491,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.select_model_mode_for_export">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">select_model_mode_for_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#select_model_mode_for_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/utils.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.select_model_mode_for_export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">select_model_mode_for_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#select_model_mode_for_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/utils.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.select_model_mode_for_export" title="Permalink to this definition">¶</a></dt>
 <dd><p>A context manager to temporarily set the training mode of <code class="docutils literal notranslate"><span class="pre">model</span></code>
 to <code class="docutils literal notranslate"><span class="pre">mode</span></code>, resetting it when we exit the with-block.</p>
 <dl class="field-list simple">
@@ -1506,7 +1506,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.is_in_onnx_export">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_in_onnx_export</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#is_in_onnx_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/utils.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.is_in_onnx_export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_in_onnx_export</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#is_in_onnx_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/utils.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.is_in_onnx_export" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether it is in the middle of ONNX export.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1517,7 +1517,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.verification.find_mismatch">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">find_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_constant_folding=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training=&lt;TrainingMode.EVAL:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/verification.html#find_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/onnx/verification.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.find_mismatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">find_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_constant_folding=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training=&lt;TrainingMode.EVAL:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/verification.html#find_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.find_mismatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find all mismatches between the original model and the exported model.</p>
 <p>Experimental. The API is subject to change.</p>
 <p>This tool helps debug the mismatch between the original PyTorch model and exported
diff --git a/main/onnx_torchscript_supported_aten_ops.html b/main/onnx_torchscript_supported_aten_ops.html
index af5bc221e84..f765e34da36 100644
--- a/main/onnx_torchscript_supported_aten_ops.html
+++ b/main/onnx_torchscript_supported_aten_ops.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/optim.html b/main/optim.html
index 078b5b69a90..0e0534c31cc 100644
--- a/main/optim.html
+++ b/main/optim.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -724,7 +724,7 @@ <h4><code class="docutils literal notranslate"><span class="pre">optimizer.step(
 <span id="optimizer-algorithms"></span><h2>Base class<a class="headerlink" href="#base-class" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Optimizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Optimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/optimizer.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Optimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for all optimizers.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1452,13 +1452,13 @@ <h3>Putting it all together: EMA<a class="headerlink" href="#putting-it-all-toge
 </table>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.get_ema_multi_avg_fn">
-<span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">get_ema_multi_avg_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/swa_utils.html#get_ema_multi_avg_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/swa_utils.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.get_ema_multi_avg_fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">get_ema_multi_avg_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/swa_utils.html#get_ema_multi_avg_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.get_ema_multi_avg_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the function applying exponential moving average (EMA) across multiple params.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.update_bn">
-<span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">update_bn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/swa_utils.html#update_bn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/optim/swa_utils.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.update_bn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">update_bn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/swa_utils.html#update_bn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.update_bn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update BatchNorm running_mean, running_var buffers in the model.</p>
 <p>It performs one pass over data in <cite>loader</cite> to estimate the activation
 statistics for BatchNorm layers in the model.</p>
diff --git a/main/package.html b/main/package.html
index 76fd87c826f..0c08b748031 100644
--- a/main/package.html
+++ b/main/package.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1299,7 +1299,7 @@ <h4>Mangling<a class="headerlink" href="#mangling" title="Permalink to this head
 <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.PackagingError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackagingError</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dependency_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackagingError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackagingError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackagingError</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dependency_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackagingError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackagingError" title="Permalink to this definition">¶</a></dt>
 <dd><p>This exception is raised when there is an issue with exporting a package.
 <code class="docutils literal notranslate"><span class="pre">PackageExporter</span></code> will attempt to gather up all the errors and present
 them to you at once.</p>
@@ -1309,14 +1309,14 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.EmptyMatchError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">EmptyMatchError</span></span><a class="reference internal" href="_modules/torch/package/package_exporter.html#EmptyMatchError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.EmptyMatchError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">EmptyMatchError</span></span><a class="reference internal" href="_modules/torch/package/package_exporter.html#EmptyMatchError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.EmptyMatchError" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is an exception that is thrown when a mock or extern is marked as
 <code class="docutils literal notranslate"><span class="pre">allow_empty=False</span></code>, and is not matched with any module during packaging.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.PackageExporter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackageExporter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importer=&lt;torch.package.importer._SysImporter</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackageExporter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importer=&lt;torch.package.importer._SysImporter</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Exporters allow you to write packages of code, pickled Python data, and
 arbitrary binary and text resources into a self-contained package.</p>
 <p>Imports can load this code in a hermetic way, such that code is loaded
@@ -1340,7 +1340,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importer=&lt;torch.package.importer._SysImporter</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importer=&lt;torch.package.importer._SysImporter</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create an exporter.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1357,7 +1357,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.add_dependency">
-<span class="sig-name descname"><span class="pre">add_dependency</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.add_dependency"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.add_dependency" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_dependency</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.add_dependency"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.add_dependency" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a module, add it to the dependency graph according to patterns
 specified by the user.</p>
 <dl class="field-list simple">
@@ -1366,7 +1366,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.all_paths">
-<span class="sig-name descname"><span class="pre">all_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.all_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L1164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.all_paths" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">all_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.all_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.all_paths" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Return a dot representation of the subgraph</dt><dd><p>that has all paths from src to dst.</p>
 </dd>
@@ -1384,7 +1384,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.close">
-<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L1067"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.close" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1067"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.close" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write the package to the filesystem. Any calls after <a class="reference internal" href="#torch.package.PackageExporter.close" title="torch.package.PackageExporter.close"><code class="xref py py-meth docutils literal notranslate"><span class="pre">close()</span></code></a> are now invalid.
 It is preferable to use resource guard syntax instead:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">PackageExporter</span><span class="p">(</span><span class="s2">&quot;file.zip&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
@@ -1395,7 +1395,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.denied_modules">
-<span class="sig-name descname"><span class="pre">denied_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.denied_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L1144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.denied_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">denied_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.denied_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.denied_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all modules that are currently denied.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1410,7 +1410,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.deny">
-<span class="sig-name descname"><span class="pre">deny</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.deny"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.deny" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">deny</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.deny"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.deny" title="Permalink to this definition">¶</a></dt>
 <dd><p>Blocklist modules who names match the given glob patterns from the list of modules the package can import.
 If a dependency on any matching packages is found, a <a class="reference internal" href="#torch.package.PackagingError" title="torch.package.PackagingError"><code class="xref py py-class docutils literal notranslate"><span class="pre">PackagingError</span></code></a> is raised.</p>
 <dl class="field-list simple">
@@ -1426,7 +1426,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.dependency_graph_string">
-<span class="sig-name descname"><span class="pre">dependency_graph_string</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.dependency_graph_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L1098"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.dependency_graph_string" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">dependency_graph_string</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.dependency_graph_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1098"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.dependency_graph_string" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns digraph string representation of dependencies in package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1440,7 +1440,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.extern">
-<span class="sig-name descname"><span class="pre">extern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.extern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.extern" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.extern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.extern" title="Permalink to this definition">¶</a></dt>
 <dd><p>Include <code class="docutils literal notranslate"><span class="pre">module</span></code> in the list of external modules the package can import.
 This will prevent dependency discovery from saving
 it in the package. The importer will load an external module directly from the standard import system.
@@ -1465,7 +1465,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.externed_modules">
-<span class="sig-name descname"><span class="pre">externed_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.externed_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L1117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.externed_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">externed_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.externed_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.externed_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all modules that are currently externed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1480,7 +1480,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.get_rdeps">
-<span class="sig-name descname"><span class="pre">get_rdeps</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.get_rdeps"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L1153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.get_rdeps" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_rdeps</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.get_rdeps"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.get_rdeps" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of all modules which depend on the module <code class="docutils literal notranslate"><span class="pre">module_name</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1494,7 +1494,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.get_unique_id">
-<span class="sig-name descname"><span class="pre">get_unique_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.get_unique_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.get_unique_id" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_unique_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.get_unique_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.get_unique_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get an id. This id is guaranteed to only be handed out once for this package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1505,7 +1505,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.intern">
-<span class="sig-name descname"><span class="pre">intern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.intern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.intern" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">intern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.intern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.intern" title="Permalink to this definition">¶</a></dt>
 <dd><p>Specify modules that should be packaged. A module must match some <code class="docutils literal notranslate"><span class="pre">intern</span></code> pattern in order to be
 included in the package and have its dependencies processed recursively.</p>
 <dl class="field-list simple">
@@ -1525,7 +1525,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.interned_modules">
-<span class="sig-name descname"><span class="pre">interned_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.interned_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L1126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.interned_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">interned_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.interned_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.interned_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all modules that are currently interned.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1540,7 +1540,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.mock">
-<span class="sig-name descname"><span class="pre">mock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.mock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.mock" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.mock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.mock" title="Permalink to this definition">¶</a></dt>
 <dd><p>Replace some required modules with a mock implementation.  Mocked modules will return a fake
 object for any attribute accessed from it. Because we copy file-by-file, the dependency resolution will sometimes
 find files that are imported by model files but whose functionality is never used
@@ -1576,7 +1576,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.mocked_modules">
-<span class="sig-name descname"><span class="pre">mocked_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.mocked_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L1135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.mocked_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mocked_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.mocked_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.mocked_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all modules that are currently mocked.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1591,7 +1591,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.register_extern_hook">
-<span class="sig-name descname"><span class="pre">register_extern_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_extern_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L735"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_extern_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_extern_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_extern_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L735"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_extern_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers an extern hook on the exporter.</p>
 <p>The hook will be called each time a module matches against an <a class="reference internal" href="#torch.package.PackageExporter.extern" title="torch.package.PackageExporter.extern"><code class="xref py py-meth docutils literal notranslate"><span class="pre">extern()</span></code></a> pattern.
 It should have the following signature:</p>
@@ -1612,7 +1612,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.register_intern_hook">
-<span class="sig-name descname"><span class="pre">register_intern_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_intern_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L773"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_intern_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_intern_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_intern_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L773"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_intern_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers an intern hook on the exporter.</p>
 <p>The hook will be called each time a module matches against an <a class="reference internal" href="#torch.package.PackageExporter.intern" title="torch.package.PackageExporter.intern"><code class="xref py py-meth docutils literal notranslate"><span class="pre">intern()</span></code></a> pattern.
 It should have the following signature:</p>
@@ -1633,7 +1633,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.register_mock_hook">
-<span class="sig-name descname"><span class="pre">register_mock_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_mock_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_mock_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_mock_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_mock_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_mock_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a mock hook on the exporter.</p>
 <p>The hook will be called each time a module matches against a <a class="reference internal" href="#torch.package.PackageExporter.mock" title="torch.package.PackageExporter.mock"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mock()</span></code></a> pattern.
 It should have the following signature:</p>
@@ -1654,7 +1654,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_binary">
-<span class="sig-name descname"><span class="pre">save_binary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_binary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L724"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_binary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_binary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_binary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L724"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_binary" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save raw bytes to the package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1669,7 +1669,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_module">
-<span class="sig-name descname"><span class="pre">save_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save the code for <code class="docutils literal notranslate"><span class="pre">module</span></code> into the package. Code for the module is resolved using the <code class="docutils literal notranslate"><span class="pre">importers</span></code> path to find the
 module object, and then using its <code class="docutils literal notranslate"><span class="pre">__file__</span></code> attribute to find the source code.</p>
 <dl class="field-list simple">
@@ -1685,7 +1685,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_pickle">
-<span class="sig-name descname"><span class="pre">save_pickle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_pickle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L583"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_pickle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_pickle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_pickle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L583"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_pickle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save a python object to the archive using pickle. Equivalent to <a class="reference internal" href="generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> but saving into
 the archive rather than a stand-alone file. Standard pickle does not save the code, only the objects.
 If <code class="docutils literal notranslate"><span class="pre">dependencies</span></code> is true, this method will also scan the pickled objects for which modules are required
@@ -1708,7 +1708,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_source_file">
-<span class="sig-name descname"><span class="pre">save_source_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_or_directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_source_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_source_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_source_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_or_directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_source_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_source_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds the local file system <code class="docutils literal notranslate"><span class="pre">file_or_directory</span></code> to the source package to provide the code
 for <code class="docutils literal notranslate"><span class="pre">module_name</span></code>.</p>
 <dl class="field-list simple">
@@ -1726,7 +1726,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_source_string">
-<span class="sig-name descname"><span class="pre">save_source_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_package</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_source_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_source_string" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_source_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_package</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_source_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_source_string" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds <code class="docutils literal notranslate"><span class="pre">src</span></code> as the source code for <code class="docutils literal notranslate"><span class="pre">module_name</span></code> in the exported package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1743,7 +1743,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_text">
-<span class="sig-name descname"><span class="pre">save_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_exporter.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_text" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_text" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save text data to the package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1760,7 +1760,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.PackageImporter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackageImporter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_or_buffer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_allowed=&lt;function</span> <span class="pre">PackageImporter.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_importer.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackageImporter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_or_buffer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_allowed=&lt;function</span> <span class="pre">PackageImporter.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Importers allow you to load code written to packages by <a class="reference internal" href="#torch.package.PackageExporter" title="torch.package.PackageExporter"><code class="xref py py-class docutils literal notranslate"><span class="pre">PackageExporter</span></code></a>.
 Code is loaded in a hermetic way, using files from the package
 rather than the normal python import system. This allows
@@ -1775,7 +1775,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_or_buffer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_allowed=&lt;function</span> <span class="pre">PackageImporter.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_importer.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_or_buffer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_allowed=&lt;function</span> <span class="pre">PackageImporter.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Open <code class="docutils literal notranslate"><span class="pre">file_or_buffer</span></code> for importing. This checks that the imported package only requires modules
 allowed by <code class="docutils literal notranslate"><span class="pre">module_allowed</span></code></p>
 <dl class="field-list simple">
@@ -1796,7 +1796,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.file_structure">
-<span class="sig-name descname"><span class="pre">file_structure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'**'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.file_structure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_importer.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.file_structure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">file_structure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'**'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.file_structure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.file_structure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a file structure representation of package’s zipfile.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1818,7 +1818,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.id">
-<span class="sig-name descname"><span class="pre">id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_importer.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.id" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns internal identifier that torch.package uses to distinguish <a class="reference internal" href="#torch.package.PackageImporter" title="torch.package.PackageImporter"><code class="xref py py-class docutils literal notranslate"><span class="pre">PackageImporter</span></code></a> instances.
 Looks like:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">torch_package_0</span><span class="o">&gt;</span>
@@ -1828,7 +1828,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.import_module">
-<span class="sig-name descname"><span class="pre">import_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">package</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.import_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_importer.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.import_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">import_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">package</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.import_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.import_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a module from the package if it hasn’t already been loaded, and then return
 the module. Modules are loaded locally
 to the importer and will appear in <code class="docutils literal notranslate"><span class="pre">self.modules</span></code> rather than <code class="docutils literal notranslate"><span class="pre">sys.modules</span></code>.</p>
@@ -1850,7 +1850,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.load_binary">
-<span class="sig-name descname"><span class="pre">load_binary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_binary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_importer.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_binary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_binary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_binary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_binary" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load raw bytes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1870,7 +1870,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.load_pickle">
-<span class="sig-name descname"><span class="pre">load_pickle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_pickle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_importer.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_pickle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_pickle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_pickle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_pickle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unpickles the resource from the package, loading any modules that are needed to construct the objects
 using <a class="reference internal" href="#torch.package.PackageImporter.import_module" title="torch.package.PackageImporter.import_module"><code class="xref py py-meth docutils literal notranslate"><span class="pre">import_module()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -1892,7 +1892,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.load_text">
-<span class="sig-name descname"><span class="pre">load_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'strict'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_importer.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_text" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'strict'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_text" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a string.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1914,7 +1914,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.python_version">
-<span class="sig-name descname"><span class="pre">python_version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.python_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/package_importer.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.python_version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">python_version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.python_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.python_version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the version of python that was used to create this package.</p>
 <p>Note: this function is experimental and not Forward Compatible. The plan is to move this into a lock
 file later on.</p>
@@ -1929,7 +1929,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.Directory">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">Directory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/file_structure_representation.html#Directory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/file_structure_representation.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.Directory" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">Directory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/file_structure_representation.html#Directory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/file_structure_representation.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.Directory" title="Permalink to this definition">¶</a></dt>
 <dd><p>A file structure representation. Organized as Directory nodes that have lists of
 their Directory children. Directories for a package are created by calling
 <a class="reference internal" href="#torch.package.PackageImporter.file_structure" title="torch.package.PackageImporter.file_structure"><code class="xref py py-meth docutils literal notranslate"><span class="pre">PackageImporter.file_structure()</span></code></a>.</p>
@@ -1937,7 +1937,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.Directory.has_file">
-<span class="sig-name descname"><span class="pre">has_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/file_structure_representation.html#Directory.has_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/package/file_structure_representation.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.Directory.has_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">has_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/file_structure_representation.html#Directory.has_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/file_structure_representation.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.Directory.has_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checks if a file is present in a <a class="reference internal" href="#torch.package.Directory" title="torch.package.Directory"><code class="xref py py-class docutils literal notranslate"><span class="pre">Directory</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/profiler.html b/main/profiler.html
index 41991ffff9f..55cc41f5140 100644
--- a/main/profiler.html
+++ b/main/profiler.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -622,7 +622,7 @@ <h1>torch.profiler<a class="headerlink" href="#torch-profiler" title="Permalink
 <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">_KinetoProfile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_trace_observer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">_KinetoProfile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_trace_observer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Low-level profiler wrap the autograd profile</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -665,7 +665,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.add_metadata">
-<span class="sig-name descname"><span class="pre">add_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds a user defined metadata with a string key and a string value
 into the trace file</p>
 <dl class="field-list simple">
@@ -674,7 +674,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.add_metadata_json">
-<span class="sig-name descname"><span class="pre">add_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata_json" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata_json" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds a user defined metadata with a string key and a valid json value
 into the trace file</p>
 <dl class="field-list simple">
@@ -683,14 +683,14 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.events">
-<span class="sig-name descname"><span class="pre">events</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.events"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.events" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">events</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.events"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.events" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the list of unaggregated profiler events,
 to be used in the trace callback or after the profiling is finished</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.export_chrome_trace">
-<span class="sig-name descname"><span class="pre">export_chrome_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_chrome_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">export_chrome_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_chrome_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Exports the collected trace in Chrome JSON format. If kineto is enabled, only
 last cycle in schedule is exported.</p>
 <dl class="field-list simple">
@@ -699,7 +699,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.export_memory_timeline">
-<span class="sig-name descname"><span class="pre">export_memory_timeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_memory_timeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L376"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_memory_timeline" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">export_memory_timeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_memory_timeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L376"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_memory_timeline" title="Permalink to this definition">¶</a></dt>
 <dd><p>Export memory event information from the profiler collected
 tree for a given device, and export a timeline plot. There are 3
 exportable files using <code class="docutils literal notranslate"><span class="pre">export_memory_timeline</span></code>, each controlled by the
@@ -724,7 +724,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.export_stacks">
-<span class="sig-name descname"><span class="pre">export_stacks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'self_cpu_time_total'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_stacks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_stacks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">export_stacks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'self_cpu_time_total'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_stacks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_stacks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save stack traces to a file</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -738,7 +738,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.key_averages">
-<span class="sig-name descname"><span class="pre">key_averages</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_by_input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_stack_n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.key_averages"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L304"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.key_averages" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">key_averages</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_by_input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_stack_n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.key_averages"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L304"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.key_averages" title="Permalink to this definition">¶</a></dt>
 <dd><p>Averages events, grouping them by operator name and (optionally) input shapes and
 stack.</p>
 <div class="admonition note">
@@ -752,7 +752,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.preset_metadata_json">
-<span class="sig-name descname"><span class="pre">preset_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.preset_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.preset_metadata_json" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">preset_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.preset_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.preset_metadata_json" title="Permalink to this definition">¶</a></dt>
 <dd><p>Preset a user defined metadata when the profiler is not started
 and added into the trace file later.
 Metadata is in the format of a string key and a valid json value</p>
@@ -762,7 +762,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.toggle_collection_dynamic">
-<span class="sig-name descname"><span class="pre">toggle_collection_dynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.toggle_collection_dynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.toggle_collection_dynamic" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">toggle_collection_dynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.toggle_collection_dynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.toggle_collection_dynamic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Toggle collection of activities on/off at any point of collection. Currently supports toggling Torch Ops
 (CPU) and CUDA activity supported in Kineto</p>
 <dl class="field-list simple">
@@ -795,7 +795,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.profiler.profile">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_trace_ready</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_trace_observer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_trace_ready</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_trace_observer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Profiler context manager.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -927,19 +927,19 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 Note: One can also pass any object satisfying the _ITraceObserver interface.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler.profile.get_trace_id">
-<span class="sig-name descname"><span class="pre">get_trace_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.get_trace_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L843"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.get_trace_id" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_trace_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.get_trace_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L843"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.get_trace_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current trace ID.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler.profile.set_custom_trace_id_callback">
-<span class="sig-name descname"><span class="pre">set_custom_trace_id_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.set_custom_trace_id_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L837"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.set_custom_trace_id_callback" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_custom_trace_id_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.set_custom_trace_id_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L837"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.set_custom_trace_id_callback" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets a callback to be called when a new trace ID is generated.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler.profile.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L815"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L815"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Signals the profiler that the next profiling step has started.</p>
 </dd></dl>
 
@@ -947,7 +947,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.profiler.ProfilerAction">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">ProfilerAction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#ProfilerAction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.ProfilerAction" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">ProfilerAction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#ProfilerAction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.ProfilerAction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Profiler actions that can be taken at the specified intervals</p>
 </dd></dl>
 
@@ -969,7 +969,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.schedule">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">schedule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warmup</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_first_wait</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#schedule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L438"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.schedule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">schedule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warmup</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_first_wait</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#schedule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L438"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.schedule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a callable that can be used as profiler <code class="docutils literal notranslate"><span class="pre">schedule</span></code> argument. The profiler will skip
 the first <code class="docutils literal notranslate"><span class="pre">skip_first</span></code> steps, then wait for <code class="docutils literal notranslate"><span class="pre">wait</span></code> steps, then do the warmup for the next <code class="docutils literal notranslate"><span class="pre">warmup</span></code> steps,
 then do the active recording for the next <code class="docutils literal notranslate"><span class="pre">active</span></code> steps and then repeat the cycle starting with <code class="docutils literal notranslate"><span class="pre">wait</span></code> steps.
@@ -990,7 +990,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.tensorboard_trace_handler">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">tensorboard_trace_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dir_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">worker_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_gzip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#tensorboard_trace_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/profiler.py#L502"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.tensorboard_trace_handler" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">tensorboard_trace_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dir_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">worker_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_gzip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#tensorboard_trace_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L502"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.tensorboard_trace_handler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Outputs tracing files to directory of <code class="docutils literal notranslate"><span class="pre">dir_name</span></code>, then that directory can be
 directly delivered to tensorboard as logdir.
 <code class="docutils literal notranslate"><span class="pre">worker_name</span></code> should be unique for each worker in distributed scenario,
@@ -1004,13 +1004,13 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 <h2>Intel Instrumentation and Tracing Technology APIs<a class="headerlink" href="#intel-instrumentation-and-tracing-technology-apis" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.itt.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/itt.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/itt.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if ITT feature is available or not</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.itt.mark">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">mark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#mark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/itt.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.mark" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">mark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#mark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/itt.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.mark" title="Permalink to this definition">¶</a></dt>
 <dd><p>Describe an instantaneous event that occurred at some point.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1021,7 +1021,7 @@ <h2>Intel Instrumentation and Tracing Technology APIs<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.itt.range_push">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_push</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_push"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/itt.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_push" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_push</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_push"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/itt.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_push" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pushes a range onto a stack of nested range span.  Returns zero-based
 depth of the range that is started.</p>
 <dl class="field-list simple">
@@ -1033,7 +1033,7 @@ <h2>Intel Instrumentation and Tracing Technology APIs<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.itt.range_pop">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_pop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/profiler/itt.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_pop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/itt.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_pop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pops a range off of a stack of nested range spans. Returns the
 zero-based depth of the range that is ended.</p>
 </dd></dl>
diff --git a/main/quantization-accuracy-debugging.html b/main/quantization-accuracy-debugging.html
index fb8e948c2f4..3fc6f14a92e 100644
--- a/main/quantization-accuracy-debugging.html
+++ b/main/quantization-accuracy-debugging.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/quantization-backend-configuration.html b/main/quantization-backend-configuration.html
index cea330adee8..24c8df7b40e 100644
--- a/main/quantization-backend-configuration.html
+++ b/main/quantization-backend-configuration.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -631,7 +631,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method adaptive_avg_pool1d of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method adaptive_avg_pool1d of type object at 0x7f5c26368780&gt;,
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     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -651,7 +651,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function adaptive_avg_pool2d at 0x7ff033551160&gt;,
+  &#39;pattern&#39;: &lt;function adaptive_avg_pool2d at 0x7f5bfeb17280&gt;,
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     {
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@@ -671,7 +671,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function adaptive_avg_pool3d at 0x7ff0335511f0&gt;,
+  &#39;pattern&#39;: &lt;function adaptive_avg_pool3d at 0x7f5bfeb17310&gt;,
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     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -696,7 +696,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method add of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method add of type object at 0x7f5c26368780&gt;,
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@@ -721,7 +721,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method avg_pool1d of type object at 0x7ff05ad9d780&gt;,
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     {
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@@ -782,7 +782,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBn1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7ff032a25700&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7f5bfdfef820&gt;,
 },
 {
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@@ -797,7 +797,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
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 },
 {
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@@ -825,7 +825,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
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-  &#39;fuser_method&#39;: &lt;function fuse_linear_bn at 0x7ff032a25820&gt;,
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 {
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@@ -839,7 +839,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
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-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7ff032a25700&gt;,
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 },
 {
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@@ -854,7 +854,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
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-  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7ff032a258b0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7f5bfdfef9d0&gt;,
 },
 {
   &#39;pattern&#39;: &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;,
@@ -878,7 +878,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
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-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7ff032a25700&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7f5bfdfef820&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.ConvTranspose3d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;),
@@ -893,7 +893,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.ConvTranspose3d&#39;&gt;,
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-  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7ff032a258b0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7f5bfdfef9d0&gt;,
 },
 {
   &#39;pattern&#39;: &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;,
@@ -926,7 +926,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method cat of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method cat of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -936,7 +936,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method clamp of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method clamp of type object at 0x7f5c26368780&gt;,
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     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -995,7 +995,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.Conv1d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv1d of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv1d of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1037,7 +1037,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.Conv2d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv2d of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv2d of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1079,7 +1079,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.Conv3d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv3d of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv3d of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1349,7 +1349,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose1d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv_transpose1d of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv_transpose1d of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1376,7 +1376,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose2d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv_transpose2d of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv_transpose2d of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
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@@ -1403,7 +1403,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose3d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv_transpose3d of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv_transpose3d of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1446,7 +1446,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function dropout at 0x7ff033551280&gt;,
+  &#39;pattern&#39;: &lt;function dropout at 0x7f5bfeb173a0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1466,7 +1466,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function elu at 0x7ff0335518b0&gt;,
+  &#39;pattern&#39;: &lt;function elu at 0x7f5bfeb179d0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1550,7 +1550,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.sparse.EmbeddingBag&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method flatten of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method flatten of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1570,7 +1570,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function group_norm at 0x7ff033556820&gt;,
+  &#39;pattern&#39;: &lt;function group_norm at 0x7f5bfeb1c940&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1635,7 +1635,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function hardsigmoid at 0x7ff0335560d0&gt;,
+  &#39;pattern&#39;: &lt;function hardsigmoid at 0x7f5bfeb1c1f0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
@@ -1675,7 +1675,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function hardswish at 0x7ff033556280&gt;,
+  &#39;pattern&#39;: &lt;function hardswish at 0x7f5bfeb1c3a0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1695,7 +1695,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function hardtanh at 0x7ff033551790&gt;,
+  &#39;pattern&#39;: &lt;function hardtanh at 0x7f5bfeb178b0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1725,7 +1725,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function instance_norm at 0x7ff033556670&gt;,
+  &#39;pattern&#39;: &lt;function instance_norm at 0x7f5bfeb1c790&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1766,7 +1766,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function interpolate at 0x7ff0334e4790&gt;,
+  &#39;pattern&#39;: &lt;function interpolate at 0x7f5bfeaad8b0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1788,7 +1788,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function layer_norm at 0x7ff033556700&gt;,
+  &#39;pattern&#39;: &lt;function layer_norm at 0x7f5bfeb1c820&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1811,7 +1811,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function leaky_relu at 0x7ff033551a60&gt;,
+  &#39;pattern&#39;: &lt;function leaky_relu at 0x7f5bfeb17b80&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2063,7 +2063,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.rnn.LSTMCell&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method matmul of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method matmul of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2133,7 +2133,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method mean of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method mean of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2168,7 +2168,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method mul of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method mul of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2183,7 +2183,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method narrow of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method narrow of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2213,7 +2213,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method pixel_shuffle of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method pixel_shuffle of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2233,7 +2233,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method pixel_unshuffle of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method pixel_unshuffle of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2264,10 +2264,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0620d3310&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5c2d69e310&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2278,10 +2278,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc1f0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47940&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7ff05ad9d780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2293,7 +2293,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7ff05ad9d780&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2316,10 +2316,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7ff032a25790&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm1d&#39;&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm1d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2331,7 +2331,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7ff032a25790&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2345,10 +2345,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc280&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce479d0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2359,10 +2359,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc310&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47a60&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7ff05ad9d780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2374,7 +2374,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7ff05ad9d780&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2397,10 +2397,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7ff032a25790&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2412,7 +2412,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7ff032a25790&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2426,10 +2426,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc3a0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47af0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2440,10 +2440,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc430&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47b80&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7ff05ad9d780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2455,7 +2455,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7ff05ad9d780&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2478,10 +2478,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7ff032a25790&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2493,7 +2493,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7ff032a25790&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2521,10 +2521,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.LinearReLU&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc4c0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47c10&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2549,7 +2549,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.LinearReLU&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc550&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47ca0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;built-in function linear&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2578,7 +2578,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function linear&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;built-in function linear&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2619,7 +2619,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2634,7 +2634,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;built-in method relu of type object at 0x7ff05ad9d780&gt;),
+  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;built-in method relu of type object at 0x7f5c26368780&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2649,7 +2649,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7ff05ad9d780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2664,7 +2664,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7ff05ad9d780&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2679,7 +2679,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7ff05ad9d780&gt;, &lt;built-in method relu of type object at 0x7ff05ad9d780&gt;),
+  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7f5c26368780&gt;, &lt;built-in method relu of type object at 0x7f5c26368780&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2709,7 +2709,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2724,7 +2724,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;built-in method relu of type object at 0x7ff05ad9d780&gt;),
+  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;built-in method relu of type object at 0x7f5c26368780&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2739,7 +2739,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7ff05ad9d780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2754,7 +2754,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7ff05ad9d780&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2769,7 +2769,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7ff05ad9d780&gt;, &lt;built-in method relu of type object at 0x7ff05ad9d780&gt;),
+  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7f5c26368780&gt;, &lt;built-in method relu of type object at 0x7f5c26368780&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2794,7 +2794,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function relu at 0x7ff033551670&gt;,
+  &#39;pattern&#39;: &lt;function relu at 0x7f5bfeb17790&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2833,10 +2833,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc5e0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47d30&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2845,7 +2845,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc670&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47dc0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2857,10 +2857,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc700&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47e50&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7ff033551670&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2869,7 +2869,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7ff0318cc790&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47ee0&gt;,
 },
 {
   &#39;pattern&#39;: &lt;class &#39;torch.nn.modules.activation.ReLU6&#39;&gt;,
@@ -2882,7 +2882,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function relu6 at 0x7ff033551820&gt;,
+  &#39;pattern&#39;: &lt;function relu6 at 0x7f5bfeb17940&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2902,7 +2902,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method repeat_interleave of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method repeat_interleave of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2984,7 +2984,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method sigmoid of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method sigmoid of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
@@ -3034,7 +3034,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method squeeze of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method squeeze of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -3064,7 +3064,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method stack of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method stack of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -3084,7 +3084,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method tanh of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method tanh of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.0078125, zero_point_exact_match=128),
@@ -3114,7 +3114,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method transpose of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method transpose of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -3134,7 +3134,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method unsqueeze of type object at 0x7ff05ad9d780&gt;,
+  &#39;pattern&#39;: &lt;built-in method unsqueeze of type object at 0x7f5c26368780&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
diff --git a/main/quantization-support.html b/main/quantization-support.html
index 23741ccdb6d..93983c2bf62 100644
--- a/main/quantization-support.html
+++ b/main/quantization-support.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/quantization.html b/main/quantization.html
index 69b67b2c046..103659997d6 100644
--- a/main/quantization.html
+++ b/main/quantization.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/random.html b/main/random.html
index b0214492a85..ba625309a94 100644
--- a/main/random.html
+++ b/main/random.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="torch-random"></span><h1>torch.random<a class="headerlink" href="#module-torch.random" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.fork_rng">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">fork_rng</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_caller</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fork_rng'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_devices_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'devices'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#fork_rng"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.fork_rng" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">fork_rng</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_caller</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fork_rng'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_devices_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'devices'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#fork_rng"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.fork_rng" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forks the RNG, so that when you return, the RNG is reset
 to the state that it was previously in.</p>
 <dl class="field-list simple">
@@ -636,7 +636,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the random number generator state as a <cite>torch.ByteTensor</cite>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -652,7 +652,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.initial_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.initial_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.initial_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the initial seed for generating random numbers as a
 Python <cite>long</cite>.</p>
 <div class="admonition note">
@@ -668,7 +668,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers on all devices. Returns a
 <cite>torch.Generator</cite> object.</p>
 <dl class="field-list simple">
@@ -686,7 +686,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers to a non-deterministic
 random number on all devices. Returns a 64 bit number used to seed the RNG.</p>
 <dl class="field-list simple">
@@ -698,7 +698,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/random.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the random number generator state.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/rpc.html b/main/rpc.html
index a1f9c64227b..f68525fce3e 100644
--- a/main/rpc.html
+++ b/main/rpc.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -693,7 +693,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 framework, RRef framework and distributed autograd.</p>
 <span class="target" id="module-torch.distributed.rpc"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.init_rpc">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">init_rpc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rpc_backend_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc.html#init_rpc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/rpc/__init__.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.init_rpc" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">init_rpc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rpc_backend_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc.html#init_rpc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/__init__.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.init_rpc" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initializes RPC primitives such as the local RPC agent
 and distributed autograd, which immediately makes the current
 process ready to send and receive RPCs.</p>
@@ -743,7 +743,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.rpc_sync">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_sync</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/rpc/api.py#L757"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_sync" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_sync</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L757"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_sync" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make a blocking RPC call to run function <code class="docutils literal notranslate"><span class="pre">func</span></code> on worker <code class="docutils literal notranslate"><span class="pre">to</span></code>. RPC
 messages are sent and received in parallel to execution of Python code. This
 method is thread-safe.</p>
@@ -818,7 +818,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.rpc_async">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_async</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_async"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/rpc/api.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_async" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_async</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_async"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_async" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make a non-blocking RPC call to run function <code class="docutils literal notranslate"><span class="pre">func</span></code> on worker <code class="docutils literal notranslate"><span class="pre">to</span></code>. RPC
 messages are sent and received in parallel to execution of Python code. This
 method is thread-safe. This method will immediately return a
@@ -915,7 +915,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.remote">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">remote</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#remote"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/rpc/api.py#L545"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.remote" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">remote</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#remote"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L545"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.remote" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make a remote call to run <code class="docutils literal notranslate"><span class="pre">func</span></code> on worker <code class="docutils literal notranslate"><span class="pre">to</span></code> and return an
 <code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> to the result value immediately.
 Worker <code class="docutils literal notranslate"><span class="pre">to</span></code> will be the owner of the returned
@@ -1024,7 +1024,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.get_worker_info">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">get_worker_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#get_worker_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/rpc/api.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.get_worker_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">get_worker_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#get_worker_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.get_worker_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get <a class="reference internal" href="#torch.distributed.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><code class="xref py py-class docutils literal notranslate"><span class="pre">WorkerInfo</span></code></a> of a given worker name.
 Use this <a class="reference internal" href="#torch.distributed.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><code class="xref py py-class docutils literal notranslate"><span class="pre">WorkerInfo</span></code></a> to avoid passing an
 expensive string on every invocation.</p>
@@ -1043,7 +1043,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.shutdown">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">shutdown</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graceful</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/rpc/api.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.shutdown" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">shutdown</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graceful</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.shutdown" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a shutdown of the RPC agent, and then destroy the RPC agent. This
 stops the local agent from accepting outstanding requests, and shuts
 down the RPC framework by terminating all RPC threads. If <code class="docutils literal notranslate"><span class="pre">graceful=True</span></code>,
@@ -1124,7 +1124,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 how a given function should be treated on the callee side.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.functions.async_execution">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.functions.</span></span><span class="sig-name descname"><span class="pre">async_execution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/functions.html#async_execution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/rpc/functions.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.functions.async_execution" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.functions.</span></span><span class="sig-name descname"><span class="pre">async_execution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/functions.html#async_execution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/functions.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.functions.async_execution" title="Permalink to this definition">¶</a></dt>
 <dd><p>A decorator for a function indicating that the return value of the function
 is guaranteed to be a <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> object and this
 function can run asynchronously on the RPC callee. More specifically, the
@@ -1367,7 +1367,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.rpc.TensorPipeRpcBackendOptions">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">TensorPipeRpcBackendOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_worker_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rpc_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">60.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'env://'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_maps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_transports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/rpc/options.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">TensorPipeRpcBackendOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_worker_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rpc_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">60.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'env://'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_maps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_transports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/options.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions" title="Permalink to this definition">¶</a></dt>
 <dd><p>The backend options for
 <code class="xref py py-class docutils literal notranslate"><span class="pre">TensorPipeAgent</span></code>, derived from
 <a class="reference internal" href="#torch.distributed.rpc.RpcBackendOptions" title="torch.distributed.rpc.RpcBackendOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">RpcBackendOptions</span></code></a>.</p>
@@ -1439,7 +1439,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.rpc.TensorPipeRpcBackendOptions.set_device_map">
-<span class="sig-name descname"><span class="pre">set_device_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions.set_device_map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/distributed/rpc/options.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_device_map" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_device_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions.set_device_map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/options.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_device_map" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set device mapping between each RPC caller and callee pair. This
 function can be called multiple times to incrementally add
 device placement configurations.</p>
@@ -1490,7 +1490,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.rpc.TensorPipeRpcBackendOptions.set_devices">
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+<span class="sig-name descname"><span class="pre">set_devices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">devices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions.set_devices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/options.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_devices" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set local devices used by the TensorPipe RPC agent. When processing
 CUDA RPC requests, the TensorPipe RPC agent will properly synchronize
 CUDA streams for all devices in this <code class="docutils literal notranslate"><span class="pre">List</span></code>.</p>
@@ -1837,7 +1837,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 asynchronously if needed via additional APIs supported by RemoteModule.</p>
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+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.nn.api.remote_module.</span></span><span class="sig-name descname"><span class="pre">RemoteModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/nn/api/remote_module.html#RemoteModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/nn/api/remote_module.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.nn.api.remote_module.RemoteModule" title="Permalink to this definition">¶</a></dt>
 <dd><blockquote>
 <div><p>A RemoteModule instance can only be created after RPC initialization.</p>
 <p>It creates a user-specified module on a specified remote node.
@@ -1918,7 +1918,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
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+<span class="sig-name descname"><span class="pre">get_module_rref</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/nn/api/remote_module.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.nn.api.remote_module.RemoteModule.get_module_rref" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an <code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> (<code class="docutils literal notranslate"><span class="pre">RRef[nn.Module]</span></code>) pointing to the remote module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1929,7 +1929,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 
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+<span class="sig-name descname"><span class="pre">remote_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/nn/api/remote_module.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.nn.api.remote_module.RemoteModule.remote_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of <code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> pointing to the remote module’s parameters.</p>
 <p>This can typically be used in conjunction
 with <a class="reference internal" href="distributed.optim.html#torch.distributed.optim.DistributedOptimizer" title="torch.distributed.optim.DistributedOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedOptimizer</span></code></a>.</p>
@@ -2010,7 +2010,7 @@ <h2>Distributed Autograd Framework<a class="headerlink" href="#distributed-autog
 
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29, 35, 36, 37, 38, 47, 55, 57, 59, 61, 62, 63, 64, 67, 68, 70, 447, 485, 495, 498, 791, 803, 865, 1003, 1081, 1202, 1203, 1218, 1228, 1229, 1328, 1331, 1332, 1417, 1529, 1530, 1635, 1683, 1684, 1699, 1759, 1772, 1774, 1825, 1828, 1843, 1844, 1862, 1863, 1927, 2039, 2095, 2104, 2107, 2129, 2130, 2132, 2133, 2136, 2137, 2139, 2141, 2144, 2152, 2155, 2157, 2158, 2159, 2160, 2170, 2175, 2176, 2180, 2188, 2189, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2199, 2202, 2203], "forc": [1, 2, 15, 19, 21, 59, 457, 909, 1001, 1013, 1056, 1081, 1223, 1318, 1330, 1334, 1843, 1844, 1862, 1945, 1982, 2095, 2097, 2100, 2104, 2115, 2129, 2133, 2142, 2175, 2179, 2188, 2191, 2192], "particular": [1, 2, 4, 8, 15, 24, 31, 35, 40, 47, 50, 51, 55, 56, 57, 68, 70, 75, 79, 86, 485, 559, 680, 890, 891, 1119, 1214, 1222, 1315, 1410, 1518, 1582, 1880, 2048, 2097, 2099, 2107, 2112, 2121, 2133, 2136, 2138, 2139, 2143, 2144, 2147, 2155, 2157, 2170, 2172, 2179, 2188, 2191, 2193, 2194, 2195, 2203, 2204, 2205, 2206], "give": [1, 4, 5, 8, 10, 13, 15, 19, 24, 25, 31, 33, 34, 36, 49, 55, 59, 68, 843, 846, 948, 949, 1026, 1163, 1165, 1166, 1168, 1172, 1181, 1206, 1207, 1208, 1229, 1316, 1329, 1330, 1345, 1352, 1509, 1510, 1511, 1587, 1634, 1635, 1789, 1829, 1843, 1844, 1862, 1993, 2036, 2095, 2097, 2118, 2120, 2128, 2130, 2133, 2136, 2137, 2139, 2141, 2142, 2144, 2148, 2155, 2157, 2170, 2188, 2190, 2191, 2193, 2200, 2204, 2205], "explicit": [1, 9, 13, 29, 55, 57, 59, 63, 68, 1077, 1228, 1269, 1302, 1700, 1935, 2016, 2095, 2100, 2101, 2119, 2129, 2133, 2136, 2139, 2158, 2173, 2174, 2198], "control": [1, 2, 3, 6, 15, 20, 24, 25, 29, 30, 31, 32, 33, 36, 38, 40, 46, 49, 50, 56, 70, 76, 79, 85, 87, 765, 813, 887, 890, 905, 909, 924, 970, 979, 1018, 1132, 1133, 1134, 1135, 1136, 1223, 1273, 1274, 1327, 1328, 1329, 1331, 1346, 1352, 1354, 1357, 1359, 1362, 1364, 1368, 1374, 1377, 1379, 1385, 1405, 1444, 1445, 1486, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1568, 1576, 1577, 1634, 1740, 1772, 1883, 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2189, 2191, 2192, 2194, 2197, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2209], "factori": [2, 3, 12, 38, 40, 44, 48, 50, 64, 834, 842, 968, 1145, 1642, 1828, 1837, 1935, 2098, 2104, 2108, 2120, 2133, 2136, 2156, 2173, 2176, 2190, 2193], "ones": [2, 4, 6, 20, 24, 29, 34, 36, 37, 38, 55, 57, 59, 64, 65, 67, 68, 70, 75, 253, 312, 401, 444, 445, 447, 514, 806, 888, 922, 938, 940, 941, 942, 943, 944, 948, 971, 983, 1001, 1128, 1136, 1158, 1202, 1209, 1210, 1213, 1312, 1315, 1327, 1336, 1362, 1378, 1387, 1495, 1515, 1524, 1528, 1535, 1536, 1554, 1582, 1586, 1587, 1589, 1591, 1597, 1634, 1643, 1644, 1645, 1679, 1740, 1758, 1762, 1771, 1772, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1806, 1807, 1820, 1822, 1825, 1835, 1836, 1880, 1899, 1916, 1934, 1939, 1964, 2017, 2021, 2039, 2051, 2098, 2100, 2102, 2104, 2108, 2119, 2120, 2130, 2131, 2133, 2136, 2145, 2147, 2148, 2155, 2156, 2157, 2158, 2160, 2165, 2167, 2169, 2171, 2172, 2173, 2176, 2197, 2202, 2203, 2204], "autograd_tensor": 2, "kwarg": [2, 6, 15, 24, 29, 30, 31, 33, 34, 35, 37, 50, 55, 56, 59, 68, 70, 579, 602, 757, 770, 772, 773, 834, 836, 838, 839, 840, 842, 845, 919, 934, 935, 1005, 1040, 1041, 1042, 1044, 1086, 1087, 1105, 1202, 1213, 1214, 1216, 1227, 1229, 1315, 1318, 1320, 1379, 1405, 1543, 1571, 1582, 1613, 1617, 1621, 1623, 1624, 1762, 1772, 1797, 1802, 1806, 1816, 1823, 1825, 1828, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 1927, 2033, 2048, 2054, 2095, 2100, 2101, 2104, 2136, 2137, 2144, 2152, 2155, 2157, 2165, 2172, 2176, 2181, 2189, 2192, 2202, 2203, 2204, 2205], "base": [2, 4, 8, 10, 13, 15, 16, 21, 24, 29, 30, 31, 33, 34, 35, 36, 37, 38, 40, 43, 48, 50, 51, 53, 55, 56, 57, 59, 64, 68, 680, 803, 808, 814, 816, 834, 835, 837, 838, 839, 840, 842, 845, 848, 862, 890, 891, 929, 932, 934, 945, 946, 955, 1001, 1026, 1106, 1107, 1122, 1143, 1145, 1188, 1235, 1246, 1269, 1279, 1317, 1329, 1341, 1379, 1390, 1392, 1394, 1402, 1495, 1518, 1555, 1582, 1585, 1586, 1587, 1591, 1597, 1599, 1626, 1628, 1630, 1705, 1740, 1771, 1772, 1778, 1779, 1780, 1787, 1789, 1797, 1801, 1803, 1845, 1862, 1863, 1892, 1902, 1927, 1939, 1997, 1998, 2015, 2025, 2027, 2049, 2096, 2098, 2100, 2104, 2121, 2132, 2133, 2135, 2136, 2139, 2144, 2159, 2160, 2165, 2166, 2170, 2171, 2172, 2174, 2175, 2177, 2180, 2181, 2183, 2190, 2191, 2192, 2194, 2196, 2203], "static": [2, 4, 9, 15, 29, 30, 35, 36, 38, 40, 50, 55, 56, 59, 70, 75, 76, 79, 680, 806, 813, 829, 831, 842, 850, 858, 886, 888, 918, 919, 920, 921, 934, 935, 1042, 1222, 1223, 1229, 1235, 1254, 1329, 1626, 1772, 1971, 2097, 2098, 2099, 2100, 2133, 2139, 2143, 2149, 2151, 2152, 2161, 2165, 2172, 2188, 2190, 2192, 2203], "Then": [2, 30, 35, 68, 957, 1528, 1634, 1792, 1837, 2037, 2130, 2131, 2133, 2135, 2136, 2137, 2147, 2148, 2155, 2157, 2158, 2165, 2166, 2180, 2185, 2188, 2191, 2192, 2195], "op": [2, 5, 6, 15, 25, 29, 31, 33, 35, 36, 55, 56, 57, 59, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 149, 502, 523, 603, 689, 690, 692, 767, 804, 805, 806, 841, 850, 883, 890, 891, 922, 929, 932, 934, 935, 943, 991, 1001, 1003, 1045, 1061, 1064, 1068, 1069, 1086, 1087, 1114, 1118, 1120, 1203, 1266, 1316, 1329, 1331, 1332, 1387, 1393, 1446, 1450, 1461, 1463, 1465, 1599, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1858, 1859, 1860, 1861, 1862, 1863, 1883, 1941, 2023, 2055, 2058, 2060, 2063, 2064, 2087, 2090, 2091, 2100, 2106, 2107, 2110, 2113, 2115, 2119, 2120, 2132, 2133, 2135, 2136, 2146, 2152, 2158, 2159, 2160, 2161, 2162, 2163, 2170, 2172, 2174, 2176, 2181, 2185, 2188, 2189, 2193, 2194, 2195, 2196, 2198, 2201, 2202, 2204], "directli": [2, 4, 8, 10, 15, 16, 24, 25, 29, 31, 33, 35, 36, 37, 38, 39, 40, 49, 55, 56, 57, 59, 63, 68, 70, 83, 556, 742, 757, 837, 919, 929, 932, 934, 1003, 1014, 1128, 1196, 1203, 1210, 1212, 1225, 1259, 1346, 1588, 1616, 1635, 1699, 1746, 1817, 2021, 2097, 2099, 2100, 2104, 2108, 2113, 2121, 2130, 2133, 2135, 2136, 2137, 2139, 2141, 2143, 2144, 2145, 2146, 2149, 2158, 2159, 2160, 2165, 2170, 2172, 2175, 2177, 2184, 2190, 2192, 2193, 2194, 2196, 2197, 2202, 2204], "ctx": [2, 6, 48, 70, 74, 918, 919, 920, 929, 930, 931, 932, 933, 934, 935, 2104, 2129, 2136, 2137, 2155, 2203], "gradcheck": [2, 1970, 2096, 2104, 2136, 2170], "extend": [2, 24, 29, 31, 35, 38, 42, 53, 68, 890, 919, 921, 929, 932, 934, 935, 1584, 1593, 1970, 2037, 2096, 2130, 2143, 2145, 2146, 2152, 2155, 2156, 2158, 2163, 2170, 2190, 2191, 2205], "staticmethod": [2, 70, 919, 921, 929, 930, 931, 932, 933, 934, 935, 2100, 2129, 2136, 2155, 2165], "result": [2, 4, 5, 6, 8, 9, 10, 13, 15, 19, 20, 24, 25, 29, 31, 35, 36, 38, 40, 43, 48, 51, 55, 56, 57, 59, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 152, 220, 221, 253, 254, 314, 334, 397, 495, 497, 498, 555, 582, 583, 584, 586, 587, 617, 696, 697, 698, 699, 700, 703, 705, 706, 707, 709, 770, 805, 809, 894, 906, 908, 927, 928, 937, 938, 939, 940, 941, 942, 948, 949, 957, 969, 972, 983, 987, 992, 998, 1000, 1001, 1003, 1004, 1013, 1014, 1018, 1022, 1024, 1051, 1053, 1055, 1056, 1086, 1087, 1101, 1124, 1125, 1126, 1127, 1128, 1140, 1145, 1163, 1166, 1172, 1181, 1188, 1190, 1193, 1198, 1199, 1202, 1203, 1204, 1207, 1208, 1213, 1214, 1223, 1227, 1238, 1243, 1252, 1255, 1259, 1277, 1278, 1280, 1292, 1306, 1315, 1318, 1319, 1327, 1331, 1332, 1334, 1336, 1337, 1341, 1355, 1357, 1359, 1360, 1368, 1372, 1374, 1376, 1377, 1378, 1385, 1395, 1396, 1403, 1413, 1415, 1416, 1417, 1418, 1420, 1421, 1473, 1476, 1491, 1492, 1493, 1518, 1520, 1521, 1522, 1526, 1528, 1533, 1547, 1552, 1582, 1588, 1598, 1626, 1627, 1628, 1629, 1630, 1634, 1670, 1688, 1699, 1755, 1758, 1759, 1772, 1789, 1792, 1825, 1828, 1829, 1830, 1831, 1837, 1840, 1875, 1880, 1883, 1885, 1892, 1893, 1896, 1902, 1915, 1921, 1936, 1951, 1963, 1974, 1976, 1978, 1982, 1989, 1991, 1992, 1993, 1996, 1997, 1998, 2010, 2014, 2015, 2020, 2021, 2023, 2024, 2026, 2036, 2039, 2043, 2044, 2048, 2069, 2082, 2097, 2098, 2099, 2100, 2104, 2107, 2108, 2110, 2119, 2120, 2121, 2128, 2129, 2130, 2131, 2132, 2133, 2135, 2136, 2137, 2138, 2141, 2142, 2144, 2146, 2147, 2148, 2149, 2151, 2152, 2153, 2155, 2157, 2159, 2160, 2161, 2162, 2165, 2166, 2169, 2170, 2172, 2173, 2175, 2176, 2177, 2181, 2182, 2184, 2189, 2190, 2191, 2193, 2197, 2200, 2201, 2202, 2203, 2204, 2205], "save_for_backward": [2, 919, 929, 931, 933, 934, 935, 2104, 2129, 2130, 2136, 2137, 2155], "grad_output": [2, 70, 918, 929, 930, 934, 935, 943, 949, 953, 954, 1315, 1582, 1880, 2098, 2130, 2133, 2136, 2137, 2144, 2198], "saved_tensor": [2, 929, 931, 932, 933, 934, 935, 2104, 2129, 2130, 2136, 2137], "inspect": [2, 29, 55, 59, 68, 960, 1315, 1327, 2129, 2136, 2143, 2158, 2161, 2173, 2194, 2196, 2197, 2203, 2205], "cost": [2, 4, 5, 9, 10, 25, 31, 33, 35, 36, 59, 485, 991, 1001, 1371, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1841, 1935, 2133, 2139, 2148, 2161, 2166, 2173, 2188, 2199, 2203, 2204], "both": [2, 3, 4, 15, 20, 24, 25, 26, 29, 30, 31, 33, 38, 43, 48, 49, 50, 51, 55, 56, 57, 59, 67, 68, 70, 78, 79, 96, 338, 498, 739, 778, 779, 782, 783, 784, 805, 834, 880, 921, 928, 934, 935, 938, 939, 940, 941, 942, 969, 1026, 1064, 1101, 1140, 1146, 1147, 1148, 1161, 1191, 1192, 1193, 1202, 1257, 1259, 1269, 1276, 1292, 1305, 1306, 1312, 1315, 1329, 1337, 1338, 1386, 1402, 1410, 1416, 1434, 1435, 1491, 1492, 1496, 1497, 1498, 1506, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1536, 1542, 1544, 1545, 1546, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1575, 1576, 1577, 1582, 1588, 1622, 1630, 1634, 1635, 1638, 1655, 1656, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1713, 1714, 1715, 1740, 1772, 1783, 1785, 1795, 1825, 1841, 1843, 1844, 1862, 1875, 1880, 1900, 1901, 1939, 1944, 1969, 1971, 1973, 1993, 1997, 2020, 2021, 2060, 2097, 2099, 2100, 2104, 2107, 2119, 2120, 2121, 2122, 2126, 2129, 2130, 2131, 2136, 2137, 2139, 2140, 2141, 2144, 2147, 2148, 2149, 2151, 2155, 2157, 2158, 2160, 2163, 2164, 2165, 2167, 2170, 2171, 2172, 2177, 2180, 2181, 2182, 2188, 2190, 2192, 2193, 2194, 2195, 2196, 2200, 2203, 2204], "cpu": [2, 5, 6, 15, 18, 24, 29, 31, 33, 36, 51, 56, 59, 70, 72, 75, 79, 80, 81, 85, 86, 118, 195, 288, 325, 334, 444, 445, 446, 447, 448, 457, 485, 579, 588, 683, 894, 909, 962, 964, 970, 979, 1024, 1027, 1028, 1039, 1051, 1052, 1053, 1055, 1146, 1148, 1158, 1163, 1181, 1193, 1197, 1199, 1200, 1264, 1266, 1267, 1268, 1273, 1274, 1291, 1315, 1323, 1325, 1326, 1335, 1345, 1347, 1351, 1352, 1353, 1354, 1356, 1358, 1361, 1363, 1364, 1370, 1373, 1376, 1379, 1380, 1386, 1387, 1402, 1405, 1416, 1428, 1444, 1518, 1524, 1582, 1755, 1772, 1778, 1779, 1780, 1787, 1816, 1817, 1831, 1834, 1842, 1880, 1895, 1904, 1906, 1908, 1910, 1911, 1935, 1940, 1941, 1942, 1944, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1963, 1973, 1979, 1980, 1981, 1982, 1983, 1984, 1997, 2014, 2015, 2016, 2025, 2027, 2032, 2036, 2053, 2093, 2096, 2097, 2098, 2100, 2104, 2108, 2110, 2115, 2118, 2119, 2121, 2126, 2133, 2136, 2137, 2139, 2142, 2144, 2147, 2148, 2149, 2150, 2155, 2156, 2157, 2159, 2161, 2164, 2165, 2170, 2172, 2173, 2176, 2177, 2179, 2182, 2184, 2188, 2192, 2193, 2194, 2196, 2197, 2200, 2201, 2203, 2204], "There": [2, 6, 7, 8, 10, 13, 15, 20, 23, 29, 31, 36, 50, 55, 56, 57, 59, 60, 64, 65, 68, 680, 744, 805, 919, 934, 935, 1001, 1203, 1518, 1552, 1598, 1740, 1772, 1781, 1782, 1823, 1828, 2095, 2097, 2099, 2100, 2106, 2107, 2120, 2121, 2130, 2133, 2136, 2137, 2138, 2143, 2146, 2149, 2150, 2151, 2155, 2158, 2159, 2160, 2161, 2165, 2167, 2172, 2176, 2179, 2184, 2188, 2190, 2191, 2193, 2194, 2201, 2202, 2203, 2204], "moment": [2, 37, 70, 76, 782, 783, 784, 1022, 1841, 1843, 1844, 1845, 1847, 1859, 1863, 2110, 2118, 2159, 2165, 2200], "nvprof": [2, 5, 958, 1056, 2133], "regist": [2, 15, 16, 20, 25, 29, 33, 34, 36, 38, 50, 53, 55, 56, 57, 59, 68, 486, 487, 680, 804, 805, 883, 953, 954, 1001, 1014, 1089, 1229, 1315, 1387, 1582, 1583, 1584, 1592, 1593, 1611, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1774, 1789, 1790, 1792, 1793, 1795, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 2037, 2039, 2104, 2113, 2118, 2121, 2133, 2135, 2136, 2143, 2144, 2149, 2152, 2155, 2158, 2165, 2182, 2191, 2193, 2196, 2199], "activ": [2, 6, 8, 10, 18, 29, 33, 35, 36, 37, 53, 55, 59, 65, 804, 806, 808, 817, 821, 870, 871, 880, 890, 891, 964, 1042, 1049, 1066, 1081, 1101, 1202, 1204, 1214, 1215, 1216, 1388, 1490, 1520, 1521, 1522, 1526, 1536, 1554, 1581, 1583, 1601, 1612, 1626, 1628, 1630, 1681, 1719, 1743, 1772, 1793, 1795, 1825, 1830, 1880, 2048, 2081, 2110, 2118, 2121, 2133, 2136, 2137, 2139, 2144, 2155, 2157, 2159, 2160, 2161, 2162, 2165, 2170, 2180, 2181, 2188, 2193, 2198, 2203, 2204], "emit_nvtx": [2, 5, 1056], "vtune": [2, 5], "emit_itt": [2, 5], "use_cuda": [2, 2159], "use_devic": 2, "record_shap": [2, 2159], "with_flop": [2, 2159], "profile_memori": [2, 2159], "with_stack": [2, 2159], "with_modul": [2, 2159], "use_kineto": 2, "use_cpu": 2, "experimental_config": [2, 2159], "acc_ev": [2, 2159], "custom_trace_id_callback": [2, 2159], "hold": [2, 29, 31, 36, 48, 50, 53, 54, 55, 59, 63, 67, 68, 536, 1042, 1388, 1528, 1583, 1584, 1592, 1593, 1634, 1762, 1772, 1775, 1776, 1792, 1802, 1816, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1979, 1980, 1981, 1982, 1983, 1984, 2131, 2133, 2136, 2138, 2143, 2146, 2157, 2159, 2165, 2166, 2167, 2169, 2170, 2172, 2173, 2175, 2176, 2190, 2191, 2194], "summari": [2, 4, 51, 1056, 1102, 1943, 2113, 2139, 2175, 2181, 2191, 2197, 2204, 2206], "hood": [2, 36, 55, 67, 2118, 2130, 2133, 2135, 2146, 2158, 2167, 2192, 2193], "just": [2, 3, 8, 15, 25, 33, 38, 40, 48, 55, 57, 68, 588, 697, 707, 802, 837, 841, 850, 918, 920, 934, 935, 957, 961, 1019, 1020, 1041, 1227, 1229, 1237, 1331, 1332, 1370, 1373, 1378, 1379, 1405, 1520, 1521, 1522, 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1092, 1094, 1096, 1097, 1099, 1100, 1101, 1102, 1110, 1111, 1115, 1146, 1148, 1197, 1223, 1229, 1232, 1235, 1238, 1239, 1261, 1424, 1426, 1427, 1439, 1445, 1458, 1459, 1460, 1464, 1904, 1908, 1910, 1935, 1979, 1980, 1981, 1982, 1983, 1984, 2014, 2054, 2060, 2061, 2076, 2077, 2079, 2080, 2081, 2083, 2084, 2115, 2118, 2121, 2130, 2135, 2139, 2142, 2146, 2152, 2153, 2159, 2173, 2187, 2188, 2190, 2193, 2201, 2204], "dealloc": [2, 68, 485, 1199, 1227, 2118, 2133, 2138, 2142, 2159], "line": [2, 5, 19, 20, 29, 40, 55, 56, 68, 680, 944, 961, 1145, 1203, 1315, 1326, 1582, 1653, 1688, 1880, 1943, 1971, 2097, 2100, 2101, 2108, 2121, 2131, 2136, 2141, 2142, 2148, 2150, 2152, 2155, 2159, 2170, 2185, 2187, 2191, 2192, 2194, 2196, 2197, 2200, 2203, 2204, 2206], "hierarchi": [2, 31, 35, 55, 68, 868, 1227, 1319, 2101, 2112, 2136, 2159, 2160, 2190], "callstack": [2, 29, 2159], "A": [2, 3, 4, 6, 8, 9, 10, 13, 15, 18, 19, 24, 25, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 40, 47, 50, 51, 53, 55, 56, 57, 59, 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2203, 2204, 2205, 2206, 2209], "aten": [2, 4, 6, 13, 15, 19, 36, 55, 56, 57, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 1086, 1203, 1836, 1837, 1971, 2097, 2098, 2104, 2115, 2121, 2132, 2136, 2150, 2152, 2156, 2159, 2160, 2170, 2182, 2185, 2188, 2189, 2195, 2197, 2201, 2204], "torchscript": [2, 4, 9, 34, 55, 56, 68, 616, 1313, 1316, 1318, 1319, 1320, 1322, 1327, 1331, 1333, 1837, 2096, 2101, 2149, 2152, 2159, 2165, 2203, 2206], "eager": [2, 9, 33, 55, 57, 1001, 1014, 1120, 1223, 1228, 1313, 1316, 1327, 1465, 1942, 2100, 2104, 2133, 2139, 2140, 2159, 2161, 2163, 2176, 2188, 2194, 2195, 2196, 2200, 2203, 2204], "experiment": [2, 3, 25, 29, 31, 34, 35, 37, 55, 56, 59, 68, 70, 73, 78, 79, 82, 232, 937, 939, 943, 1001, 1011, 1119, 1203, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1346, 1357, 1359, 1360, 1364, 1377, 1740, 1772, 1863, 2096, 2097, 2099, 2104, 2108, 2119, 2120, 2133, 2135, 2140, 2148, 2150, 2152, 2155, 2158, 2159, 2183, 2185, 2190, 2204], "kineto": [2, 957, 2159], "_experimentalconfig": [2, 2159], "librari": [2, 3, 4, 5, 9, 10, 12, 15, 16, 18, 20, 21, 24, 29, 39, 48, 53, 55, 57, 60, 63, 64, 65, 68, 680, 957, 1024, 1040, 1067, 1071, 1075, 1086, 1196, 1328, 1359, 1405, 1963, 2062, 2066, 2069, 2110, 2113, 2132, 2133, 2135, 2136, 2137, 2138, 2143, 2144, 2146, 2147, 2150, 2155, 2158, 2159, 2160, 2165, 2178, 2184, 2188, 2191, 2194, 2203, 2205, 2207], "functionev": [2, 2159], "across": [2, 9, 15, 20, 21, 24, 25, 29, 31, 33, 34, 35, 36, 37, 40, 51, 59, 64, 68, 85, 616, 745, 903, 962, 1041, 1055, 1100, 1101, 1145, 1159, 1214, 1326, 1331, 1417, 1444, 1495, 1518, 1528, 1570, 1588, 1590, 1622, 1634, 1658, 1661, 1690, 1706, 1755, 1772, 1802, 1806, 1830, 1880, 1927, 2018, 2048, 2081, 2096, 2097, 2113, 2119, 2126, 2130, 2135, 2138, 2143, 2144, 2146, 2147, 2148, 2157, 2159, 2161, 2165, 2166, 2172, 2175, 2181, 2183, 2188, 2190, 2194, 2204], "cycl": [2, 485, 922, 1040, 1075, 1229, 1868, 1875, 2069, 2130, 2159], "100": [2, 20, 24, 29, 34, 38, 68, 70, 78, 79, 84, 299, 692, 748, 749, 752, 753, 957, 1056, 1276, 1315, 1327, 1333, 1371, 1386, 1393, 1402, 1417, 1467, 1486, 1494, 1495, 1496, 1497, 1498, 1510, 1511, 1513, 1514, 1516, 1517, 1544, 1545, 1546, 1582, 1589, 1591, 1622, 1631, 1670, 1671, 1724, 1846, 1864, 1865, 1871, 1872, 1873, 1874, 1876, 1878, 1879, 1880, 1897, 1952, 2034, 2098, 2100, 2118, 2132, 2133, 2155, 2157, 2161, 2167, 2169, 2170, 2175, 2189, 2192, 2197, 2203], "realli": [2, 8, 55, 68, 1227, 1229, 2100, 2121, 2130, 2158, 2193], "y": [2, 6, 12, 15, 24, 36, 38, 47, 55, 56, 57, 59, 64, 65, 68, 70, 73, 75, 76, 77, 78, 79, 80, 81, 82, 485, 616, 619, 791, 913, 915, 916, 917, 927, 928, 929, 932, 934, 935, 937, 938, 939, 940, 941, 942, 944, 945, 964, 983, 989, 1019, 1026, 1056, 1086, 1087, 1128, 1139, 1145, 1149, 1202, 1204, 1207, 1208, 1209, 1213, 1214, 1255, 1322, 1327, 1331, 1384, 1393, 1394, 1417, 1494, 1495, 1496, 1497, 1498, 1499, 1515, 1517, 1536, 1541, 1542, 1544, 1545, 1546, 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2191, 2193, 2194, 2199, 2202, 2203, 2204], "remov": [2, 4, 24, 25, 29, 31, 33, 38, 51, 54, 59, 68, 486, 487, 512, 545, 556, 707, 810, 829, 830, 831, 888, 953, 954, 957, 964, 968, 991, 992, 1089, 1203, 1260, 1312, 1315, 1319, 1405, 1406, 1410, 1503, 1504, 1505, 1582, 1583, 1592, 1690, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1790, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1809, 1810, 1811, 1812, 1814, 1815, 1825, 1827, 1830, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 1895, 1911, 1933, 1988, 1997, 2023, 2030, 2032, 2095, 2110, 2121, 2122, 2130, 2136, 2144, 2149, 2155, 2156, 2158, 2167, 2172, 2177, 2191, 2194, 2196, 2199, 2202], "breviti": [2, 68, 964, 1026, 2121, 2155], "print": [2, 4, 6, 13, 19, 24, 26, 29, 35, 36, 40, 42, 47, 48, 51, 55, 56, 57, 59, 64, 67, 232, 730, 738, 739, 744, 755, 756, 766, 774, 951, 953, 954, 964, 1056, 1202, 1203, 1212, 1229, 1315, 1319, 1322, 1323, 1327, 1405, 1499, 1543, 1569, 1582, 1594, 1595, 1670, 1727, 1730, 1731, 1795, 1805, 1806, 1807, 1808, 1811, 1821, 1825, 1837, 1865, 1866, 1867, 1868, 1869, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1879, 1880, 1943, 2040, 2095, 2097, 2098, 2104, 2108, 2112, 2121, 2126, 2130, 2132, 2133, 2136, 2140, 2144, 2145, 2153, 2154, 2155, 2156, 2158, 2159, 2165, 2175, 2176, 2180, 2181, 2184, 2185, 2188, 2189, 2191, 2192, 2194, 2196, 2204], "tabl": [2, 4, 29, 36, 55, 68, 964, 1003, 1524, 1679, 2098, 2100, 2136, 2157, 2158, 2159, 2160, 2165, 2170, 2177, 2193, 2195, 2197, 2200, 2204], "sort_bi": [2, 964, 2159], "self_cpu_time_tot": [2, 964, 2159], "avg": [2, 29, 36, 964, 1840], "mul": [2, 19, 55, 57, 68, 70, 73, 76, 78, 419, 757, 758, 767, 964, 1056, 1292, 1469, 2097, 2098, 2100, 2119, 2136, 2156, 2162, 2166, 2170, 2173, 2180, 2189, 2190, 2191, 2198, 2202], "32": [2, 3, 25, 29, 55, 86, 756, 1199, 1312, 1325, 1405, 1492, 1520, 1521, 1522, 1526, 1527, 1529, 1530, 1550, 1570, 1576, 1587, 1626, 1627, 1628, 1629, 1630, 1642, 1653, 1683, 1684, 1690, 1740, 2120, 2121, 2144, 2148, 2155, 2163, 2170, 2173, 2175, 2176, 2191, 2192, 2197], "048m": 2, "200": [2, 34, 1315, 1393, 1582, 1880, 1897, 2100, 2170, 2191, 2204], "27": [2, 616, 1383, 1867, 1892, 2042, 2155], "041m": 2, "powbackward0": [2, 964], "9": [2, 8, 24, 25, 29, 39, 68, 70, 75, 312, 314, 316, 320, 400, 401, 470, 512, 559, 580, 582, 707, 755, 756, 883, 985, 991, 995, 999, 1026, 1127, 1128, 1143, 1146, 1167, 1168, 1177, 1178, 1188, 1191, 1192, 1195, 1196, 1269, 1279, 1345, 1347, 1368, 1370, 1372, 1383, 1385, 1477, 1478, 1484, 1485, 1488, 1489, 1502, 1504, 1524, 1525, 1578, 1579, 1594, 1595, 1605, 1608, 1679, 1680, 1727, 1730, 1731, 1830, 1831, 1840, 1843, 1844, 1845, 1846, 1847, 1859, 1862, 1863, 1864, 1868, 1875, 1877, 1878, 1880, 1881, 1884, 1888, 1924, 1931, 1940, 1943, 1953, 1958, 1968, 1976, 1985, 2014, 2015, 2019, 2021, 2030, 2042, 2049, 2097, 2101, 2107, 2115, 2138, 2140, 2144, 2149, 2150, 2155, 2156, 2157, 2158, 2165, 2170, 2171, 2173, 2177, 2191, 2203], "727m": 2, "55": [2, 1535, 2155], "483m": 2, "accumulategrad": [2, 964, 2130], "148m": 2, "graphroot": [2, 964], "691": 2, "816u": 2, "emit": [2, 15, 42, 47, 57, 68, 680, 1229, 1331, 1945, 2100, 2106, 2164, 2170, 2188, 2203], "nvtx": [2, 5], "program": [2, 4, 5, 9, 13, 19, 24, 28, 29, 36, 42, 51, 54, 55, 56, 59, 68, 616, 833, 957, 1019, 1020, 1092, 1094, 1203, 1242, 1939, 2076, 2077, 2097, 2099, 2101, 2114, 2130, 2133, 2138, 2143, 2145, 2146, 2148, 2149, 2150, 2152, 2155, 2160, 2175, 2182, 2183, 2185, 2188, 2190, 2191, 2192, 2193, 2194, 2196, 2199, 2201, 2204, 2206], "off": [2, 6, 8, 9, 15, 20, 28, 29, 33, 40, 59, 68, 848, 945, 946, 970, 979, 1056, 1106, 1203, 1273, 1274, 1312, 1331, 1379, 1491, 1492, 1493, 1495, 1575, 1576, 1577, 1688, 1727, 2132, 2133, 2135, 2138, 2143, 2147, 2148, 2159, 2160, 2161, 2165, 2166, 2190, 2193, 2200], "o": [2, 25, 29, 30, 31, 38, 40, 51, 53, 54, 55, 68, 1056, 1220, 1224, 1226, 1315, 1387, 1434, 1435, 1436, 1553, 1582, 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1981, 1983, 1984, 1985, 1993, 1997, 2016, 2018, 2021, 2046, 2047, 2099, 2100, 2108, 2113, 2119, 2130, 2133, 2136, 2141, 2148, 2152, 2158, 2159, 2170, 2176, 2177, 2189, 2191, 2197, 2203, 2204], "stdin": [2, 29, 944, 1971, 2108, 2136, 2148, 2170], "instal": [2, 3, 4, 15, 16, 29, 36, 68, 1145, 1229, 2095, 2144, 2145, 2152, 2153, 2154, 2155, 2158, 2165, 2175, 2184, 2189, 2190, 2191, 2192, 2199, 2203], "_tensor": [2, 36, 153], "py": [2, 5, 15, 19, 29, 34, 35, 38, 42, 44, 49, 51, 56, 57, 59, 68, 889, 1056, 1772, 2095, 2097, 2100, 2106, 2121, 2135, 2141, 2143, 2146, 2155, 2158, 2159, 2160, 2166, 2184, 2185, 2187, 2189, 2190, 2191, 2192, 2193, 2194, 2196, 2197, 2200, 2202, 2203, 2204, 2206], "93": [2, 616, 1127], "retain_graph": [2, 149, 922, 943, 953, 954, 1214, 2048, 2098, 2129, 2130, 2136, 2165], "90": [2, 1129, 1879, 1923, 2104, 2115], "allow_unreach": 2, "76": 2, "_forward_cl": 2, "tmp": [2, 4, 15, 29, 48, 50, 2095, 2133, 2159, 2185, 2188, 2197, 2203], "53": [2, 480], "44": [2, 320, 444, 1148, 1493, 1551, 1577, 1823, 2192], "set_detect_anomali": 2, "behaviour": [2, 55, 703, 704, 709, 1688, 1727, 1875, 1945, 2095, 2147, 2172], "interpos": [2, 2136], "grad_fn": [2, 149, 334, 487, 909, 922, 937, 938, 939, 940, 941, 942, 951, 953, 954, 1202, 1790, 1973, 2121, 2130, 2136, 2144, 2149], "node": [2, 29, 33, 34, 40, 43, 44, 49, 50, 55, 59, 68, 70, 80, 81, 680, 812, 833, 836, 838, 839, 842, 845, 850, 866, 929, 964, 1089, 1212, 1227, 1229, 1247, 1252, 1330, 1501, 1518, 1772, 1837, 2110, 2133, 2155, 2158, 2165, 2166, 2167, 2179, 2181, 2183, 2185, 2190, 2191, 2193, 2194, 2200, 2202, 2204], "grad_mod": [2, 944, 945, 946, 2098], "least": [2, 6, 7, 9, 25, 31, 33, 38, 49, 50, 59, 259, 401, 707, 834, 972, 1133, 1135, 1136, 1145, 1186, 1187, 1197, 1228, 1269, 1277, 1278, 1312, 1338, 1361, 1388, 1410, 1472, 1486, 1772, 1817, 2095, 2100, 2120, 2128, 2130, 2131, 2133, 2138, 2139, 2161, 2167, 2170, 2171, 2172, 2177, 2194, 2196, 2203], "intermediari": [2, 15, 38, 929, 932, 934, 2130, 2141, 2191, 2194], "access": [2, 10, 15, 19, 24, 29, 31, 35, 55, 56, 67, 70, 72, 487, 556, 744, 929, 932, 934, 935, 967, 1048, 1056, 1315, 1318, 1331, 1378, 1486, 1582, 1762, 1775, 1776, 1790, 1795, 1817, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 2070, 2097, 2098, 2100, 2101, 2104, 2110, 2112, 2114, 2116, 2118, 2120, 2121, 2130, 2133, 2138, 2141, 2143, 2144, 2149, 2165, 2170, 2172, 2173, 2174, 2176, 2184, 2188, 2190, 2191, 2192, 2193, 2194, 2196, 2199, 2203, 2209], "isinst": [2, 24, 38, 68, 951, 953, 954, 1302, 1313, 2098, 2100, 2130, 2136, 2144, 2157, 2158, 2170, 2194, 2202, 2204], "dir": [2, 1086, 1837, 2095, 2100, 2158], "__call__": [2, 1315, 1582, 1880, 2191], "__class__": [2, 44, 70], "__delattr__": 2, "__dir__": 2, "__doc__": 2, "__eq__": 2, "__format__": [2, 2101], "__ge__": 2, "__getattribute__": 2, "__gt__": 2, "__hash__": [2, 2101], "__init_subclass__": 2, "__le__": 2, "__lt__": [2, 2100], "__ne__": 2, "__new__": [2, 2099, 2101], "__reduce__": [2, 2158], "__reduce_ex__": 2, "__repr__": [2, 4, 2136], "__setattr__": 2, "__sizeof__": 2, "__str__": [2, 68, 2098, 2100], "__subclasshook__": 2, "_raw_saved_result": 2, "_register_hook_dict": 2, "_saved_result": [2, 2130], "metadata": [2, 4, 6, 13, 31, 43, 44, 55, 57, 70, 78, 79, 955, 1018, 1387, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1927, 2104, 2108, 2121, 2130, 2136, 2137, 2149, 2158, 2159, 2165, 2166, 2170, 2172, 2175, 2193, 2202], "next_funct": 2, "register_prehook": [2, 2130], "allclos": [2, 64, 65, 68, 948, 949, 1086, 1167, 1168, 1174, 1175, 1203, 1204, 1206, 1207, 1208, 1209, 1213, 1214, 1363, 1365, 1376, 1378, 1381, 1382, 1407, 1795, 1822, 1895, 2048, 2098, 2104, 2137, 2156, 2170], "pack": [2, 34, 748, 749, 750, 751, 752, 753, 755, 756, 770, 791, 1331, 1332, 1407, 1518, 1533, 1552, 1598, 1816, 1817, 1818, 1819, 2098, 2110, 2121, 2130, 2138, 2150, 2160, 2191], "unpack": [2, 70, 75, 76, 770, 925, 928, 1331, 1363, 1407, 1518, 1819, 1821, 1828, 1838, 2100, 2101, 2130, 2136, 2138], "hook": [2, 29, 30, 33, 34, 36, 59, 486, 487, 758, 767, 929, 932, 934, 953, 954, 957, 1089, 1315, 1518, 1582, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1791, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1808, 1813, 1824, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 2096, 2122, 2135, 2136, 2143, 2151, 2152, 2157, 2158, 2160, 2192, 2193, 2194, 2195], "common": [2, 4, 9, 24, 35, 40, 50, 64, 70, 695, 805, 894, 974, 977, 982, 1003, 1021, 1140, 1145, 1190, 1191, 1192, 1193, 1239, 1257, 1338, 1387, 1467, 1496, 1497, 1498, 1501, 1622, 1670, 1763, 1764, 1766, 1767, 1768, 1769, 1770, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1915, 1927, 1994, 2039, 2100, 2105, 2120, 2121, 2130, 2133, 2136, 2137, 2138, 2144, 2146, 2149, 2158, 2169, 2170, 2171, 2172, 2174, 2177, 2188, 2189, 2190, 2194, 2199, 2201, 2202, 2203, 2204, 2206], "trade": [2, 6, 9, 33, 59, 1379, 1495, 1939, 2132, 2138, 2161], "leav": [2, 9, 40, 51, 57, 149, 922, 1222, 1320, 1333, 1793, 1796, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1988, 2097, 2099, 2100, 2108, 2130, 2133, 2152, 2158, 2161, 2191, 2203], "especi": [2, 10, 12, 24, 29, 51, 56, 68, 253, 894, 983, 1001, 1012, 1758, 2099, 2108, 2130, 2136, 2137, 2147, 2149, 2160, 2165, 2170, 2188, 2193, 2194, 2196, 2203], "notic": [2, 13, 29, 56, 69, 699, 833, 1165, 1179, 1223, 1410, 1420, 1494, 1635, 1705, 1865, 1866, 1872, 1873, 1879, 2032, 2096, 2097, 2125, 2130, 2170, 2185, 2191, 2194, 2197, 2199, 2201], "fit": [2, 10, 42, 63, 64, 498, 961, 1312, 1846, 1917, 2133, 2152, 2177, 2204], "evalu": [2, 5, 9, 10, 33, 34, 38, 55, 57, 62, 68, 703, 709, 811, 892, 948, 1013, 1101, 1209, 1210, 1219, 1227, 1229, 1254, 1315, 1486, 1490, 1496, 1497, 1498, 1519, 1536, 1544, 1545, 1546, 1554, 1582, 1596, 1601, 1622, 1732, 1740, 1755, 1793, 1846, 1868, 1880, 1902, 2100, 2101, 2136, 2144, 2151, 2152, 2170, 2179, 2182, 2190, 2192, 2193], "saved_tensors_hook": [2, 929, 932, 934, 2130], "pack_hook": [2, 2130], "unpack_hook": [2, 2130], "pair": [2, 29, 31, 37, 38, 50, 53, 55, 616, 745, 894, 968, 989, 1026, 1179, 1225, 1229, 1329, 1379, 1417, 1574, 1583, 1588, 1592, 1729, 1861, 1911, 2099, 2100, 2106, 2119, 2126, 2130, 2133, 2165, 2166, 2167, 2175, 2177, 2181, 2194, 2201, 2203], "retriev": [2, 24, 25, 29, 34, 35, 50, 68, 86, 536, 918, 919, 934, 935, 1056, 1227, 1246, 1524, 1528, 1634, 1679, 1772, 1817, 1837, 1866, 1971, 2121, 2130, 2143, 2155, 2158, 2165, 2166, 2167, 2184, 2192, 2206], "everytim": 2, "store": [2, 4, 6, 15, 19, 25, 31, 35, 40, 44, 51, 55, 56, 57, 59, 68, 325, 331, 397, 696, 840, 845, 919, 934, 981, 1051, 1053, 1055, 1231, 1259, 1315, 1321, 1323, 1326, 1346, 1357, 1358, 1359, 1388, 1393, 1405, 1496, 1497, 1498, 1524, 1582, 1611, 1772, 1789, 1792, 1805, 1806, 1807, 1809, 1810, 1811, 1812, 1816, 1880, 1939, 1976, 2095, 2097, 2104, 2108, 2120, 2121, 2122, 2135, 2136, 2137, 2138, 2143, 2157, 2158, 2160, 2165, 2166, 2167, 2170, 2172, 2175, 2176, 2181, 2184, 2185, 2191, 2192, 2193, 2196, 2208], "content": [2, 4, 8, 20, 31, 36, 43, 55, 56, 68, 929, 932, 934, 953, 1323, 1326, 1346, 1357, 1359, 1364, 1377, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1988, 2020, 2041, 2095, 2100, 2111, 2118, 2130, 2133, 2146, 2157, 2165, 2174, 2175, 2176, 2188, 2199, 2203, 2204], "equal": [2, 13, 20, 25, 29, 38, 50, 51, 53, 55, 68, 259, 498, 544, 616, 680, 704, 705, 706, 749, 750, 751, 752, 753, 770, 778, 779, 838, 839, 840, 845, 890, 905, 970, 977, 979, 990, 991, 1015, 1026, 1053, 1055, 1072, 1115, 1134, 1150, 1189, 1204, 1205, 1222, 1225, 1229, 1239, 1240, 1255, 1258, 1273, 1274, 1276, 1277, 1278, 1300, 1304, 1340, 1361, 1374, 1378, 1381, 1382, 1405, 1406, 1417, 1439, 1479, 1483, 1484, 1485, 1487, 1488, 1489, 1494, 1495, 1501, 1510, 1511, 1513, 1514, 1518, 1525, 1528, 1529, 1530, 1533, 1535, 1552, 1587, 1588, 1596, 1598, 1634, 1643, 1656, 1657, 1661, 1664, 1667, 1679, 1680, 1683, 1684, 1690, 1697, 1772, 1820, 1880, 1883, 1896, 1952, 1953, 1958, 1985, 1993, 2015, 2031, 2037, 2051, 2098, 2101, 2107, 2119, 2120, 2121, 2130, 2131, 2136, 2141, 2147, 2156, 2157, 2171, 2172, 2175, 2177, 2191, 2202], "term": [2, 9, 10, 38, 50, 56, 57, 68, 557, 744, 773, 804, 991, 1013, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1176, 1177, 1178, 1181, 1193, 1229, 1236, 1347, 1486, 1494, 1535, 1542, 1552, 1553, 1587, 1596, 1614, 1685, 1697, 1732, 1744, 1772, 1789, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1863, 1915, 1951, 1959, 2096, 2100, 2121, 2129, 2130, 2136, 2137, 2138, 2141, 2146, 2155, 2157, 2158, 2160, 2166, 2170, 2182, 2190, 2191, 2194, 2203], "mulbackward0": [2, 938, 941, 942, 2136], "inplac": [2, 31, 55, 62, 64, 68, 70, 78, 79, 765, 768, 786, 788, 790, 803, 810, 828, 833, 865, 867, 868, 886, 887, 892, 929, 930, 934, 955, 1001, 1203, 1315, 1490, 1500, 1519, 1520, 1521, 1522, 1523, 1526, 1538, 1539, 1540, 1568, 1581, 1582, 1601, 1602, 1603, 1610, 1612, 1625, 1654, 1662, 1673, 1674, 1675, 1676, 1677, 1681, 1692, 1693, 1694, 1703, 1719, 1734, 1735, 1738, 1741, 1743, 1753, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1851, 1852, 1859, 1860, 1861, 1862, 1863, 1880, 2036, 2098, 2107, 2130, 2136, 2155, 2181, 2198, 2202], "lead": [2, 6, 8, 23, 24, 29, 39, 56, 59, 65, 68, 894, 929, 930, 934, 937, 939, 943, 949, 1003, 1196, 1214, 1219, 1229, 1346, 1552, 1598, 1614, 1880, 1982, 1988, 2048, 2100, 2104, 2120, 2121, 2130, 2132, 2135, 2136, 2137, 2139, 2146, 2147, 2150, 2157, 2158, 2160, 2165, 2170, 2175, 2176, 2188, 2195, 2203], "undefin": [2, 29, 36, 39, 43, 318, 470, 616, 907, 929, 933, 934, 935, 948, 949, 977, 985, 1003, 1148, 1198, 1199, 1415, 1494, 1836, 1889, 2104, 2126, 2130, 2133, 2136, 2137, 2148, 2165], "recurs": [2, 33, 38, 55, 57, 59, 68, 1003, 1007, 1137, 1227, 1236, 1255, 1315, 1327, 1329, 1582, 1781, 1782, 1866, 1878, 1880, 2099, 2136, 2144, 2158, 2165, 2191, 2192, 2194, 2195, 2203], "inner": [2, 4, 59, 60, 65, 829, 831, 937, 1128, 1204, 1208, 1213, 1226, 1234, 2021, 2098, 2136, 2156, 2165, 2181, 2203], "save_on_cpu": 2, "pin_memori": [2, 24, 33, 70, 72, 75, 79, 80, 81, 444, 445, 446, 447, 448, 1146, 1148, 1197, 1904, 1908, 1910, 1979, 1980, 1981, 1982, 1983, 1984, 2014, 2097, 2098, 2104, 2121, 2133, 2136, 2156, 2172, 2198], "within": [2, 6, 10, 13, 20, 24, 25, 29, 34, 35, 36, 37, 38, 50, 51, 53, 55, 56, 59, 67, 68, 86, 87, 487, 907, 924, 948, 949, 955, 1001, 1018, 1044, 1045, 1128, 1218, 1219, 1227, 1233, 1315, 1319, 1408, 1445, 1446, 1491, 1492, 1493, 1501, 1520, 1521, 1522, 1526, 1528, 1575, 1576, 1577, 1582, 1588, 1622, 1634, 1688, 1698, 1713, 1714, 1715, 1772, 1793, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1807, 1809, 1810, 1811, 1812, 1813, 1880, 1900, 1901, 1931, 1952, 2035, 2039, 2054, 2055, 2097, 2099, 2100, 2112, 2113, 2118, 2126, 2128, 2132, 2133, 2136, 2142, 2143, 2144, 2147, 2149, 2155, 2157, 2158, 2160, 2163, 2164, 2165, 2166, 2172, 2175, 2179, 2184, 2190, 2191, 2194, 2196, 2202, 2203, 2204], "move": [2, 6, 8, 9, 10, 15, 29, 31, 55, 59, 66, 68, 85, 523, 588, 839, 1165, 1236, 1315, 1323, 1382, 1387, 1423, 1444, 1496, 1497, 1498, 1555, 1556, 1557, 1582, 1622, 1727, 1775, 1776, 1860, 1880, 2099, 2110, 2111, 2118, 2120, 2126, 2133, 2134, 2137, 2138, 2144, 2145, 2146, 2149, 2157, 2158, 2160, 2165, 2172, 2173, 2177, 2194, 2204], "copi": [2, 8, 12, 24, 25, 29, 31, 33, 40, 48, 55, 59, 63, 64, 68, 189, 195, 205, 208, 314, 401, 447, 457, 462, 470, 492, 498, 579, 580, 581, 582, 602, 616, 622, 804, 828, 865, 867, 908, 909, 927, 944, 945, 946, 968, 990, 998, 1001, 1051, 1052, 1184, 1185, 1186, 1187, 1189, 1203, 1229, 1315, 1327, 1387, 1388, 1478, 1518, 1528, 1582, 1592, 1634, 1772, 1816, 1826, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1851, 1859, 1860, 1861, 1862, 1863, 1880, 1912, 1918, 1924, 2014, 2023, 2029, 2097, 2098, 2108, 2118, 2121, 2130, 2131, 2133, 2136, 2139, 2146, 2156, 2157, 2158, 2159, 2160, 2165, 2170, 2172, 2173, 2174, 2176, 2181, 2185, 2188, 2193, 2194, 2196, 2198, 2202, 2203], "pin": [2, 31, 33, 208, 336, 444, 445, 446, 447, 448, 462, 579, 602, 622, 1146, 1148, 1197, 1315, 1582, 1816, 1880, 1904, 1908, 1910, 1979, 1980, 1981, 1982, 1983, 1984, 2014, 2121, 2149, 2172, 2179], "asynchron": [2, 4, 5, 31, 36, 67, 87, 195, 208, 579, 602, 622, 1066, 1315, 1318, 1334, 1445, 1582, 1880, 2101, 2132, 2135, 2165, 2172, 2175, 2179, 2201, 2208], "prod_1": 2, "prod_2": 2, "del": [2, 35, 55, 485, 2101, 2118, 2136, 2138], "illustr": [2, 2100, 2129, 2136, 2170, 2172, 2184, 2188, 2197], "aliv": [2, 6, 24, 40, 50, 1040, 1075, 2069, 2130, 2133, 2138, 2146, 2165, 2166, 2167], "live": [2, 34, 485, 1086, 1089, 1315, 1582, 1880, 2097, 2133, 2138, 2139, 2165, 2167, 2188, 2191, 2193, 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1368, 1369, 1372, 1381, 1382, 1383, 1384, 1385, 1386, 1388, 1395, 1402, 1403, 1408, 1413, 1415, 1416, 1418, 1421, 1423, 1424, 1425, 1426, 1429, 1430, 1437, 1440, 1445, 1447, 1448, 1449, 1450, 1451, 1453, 1454, 1458, 1459, 1460, 1461, 1462, 1468, 1473, 1474, 1475, 1476, 1477, 1478, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1491, 1492, 1493, 1496, 1497, 1498, 1499, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1516, 1517, 1518, 1524, 1525, 1527, 1528, 1529, 1530, 1532, 1534, 1536, 1544, 1545, 1546, 1549, 1550, 1551, 1553, 1554, 1558, 1559, 1560, 1561, 1562, 1563, 1567, 1569, 1570, 1572, 1575, 1576, 1577, 1578, 1579, 1580, 1582, 1584, 1587, 1589, 1590, 1593, 1594, 1595, 1597, 1600, 1604, 1605, 1606, 1607, 1608, 1609, 1616, 1618, 1622, 1626, 1627, 1628, 1629, 1630, 1631, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1670, 1671, 1672, 1679, 1680, 1687, 1690, 1699, 1707, 1724, 1725, 1726, 1730, 1731, 1746, 1747, 1755, 1759, 1760, 1761, 1771, 1772, 1790, 1791, 1800, 1801, 1803, 1804, 1806, 1809, 1810, 1811, 1812, 1816, 1817, 1819, 1824, 1827, 1830, 1831, 1833, 1834, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1865, 1866, 1867, 1868, 1869, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1885, 1886, 1893, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1904, 1906, 1907, 1908, 1910, 1916, 1917, 1918, 1921, 1922, 1923, 1924, 1927, 1932, 1933, 1934, 1938, 1941, 1942, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1964, 1968, 1972, 1975, 1976, 1978, 1985, 1988, 1990, 1991, 1992, 1993, 1996, 1998, 2003, 2005, 2011, 2015, 2016, 2018, 2020, 2021, 2024, 2025, 2026, 2027, 2030, 2031, 2032, 2033, 2034, 2035, 2038, 2039, 2042, 2043, 2044, 2048, 2049, 2054, 2056, 2057, 2058, 2059, 2063, 2064, 2065, 2067, 2069, 2071, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2083, 2084, 2087, 2088, 2092, 2093, 2097, 2098, 2099, 2100, 2101, 2104, 2113, 2118, 2119, 2121, 2126, 2128, 2133, 2137, 2146, 2149, 2152, 2155, 2156, 2159, 2160, 2164, 2165, 2169, 2171, 2172, 2173, 2175, 2176, 2177, 2179, 2184, 2190, 2194, 2198, 2202, 2203, 2206, 2209], "show": [3, 5, 8, 14, 19, 24, 25, 29, 31, 35, 39, 55, 57, 59, 68, 937, 943, 1001, 1196, 1315, 1417, 1582, 1762, 1837, 1880, 2095, 2100, 2119, 2121, 2132, 2133, 2135, 2136, 2141, 2142, 2144, 2151, 2152, 2155, 2157, 2158, 2159, 2165, 2167, 2172, 2188, 2191, 2196, 2197, 2200, 2201, 2204, 2206], "max_siz": [3, 49, 51, 2133], "capac": [3, 1115, 2133, 2146], "preferred_blas_librari": 3, "overrid": [3, 6, 15, 20, 21, 25, 29, 30, 31, 33, 36, 38, 43, 51, 55, 57, 59, 64, 68, 804, 805, 883, 919, 921, 934, 935, 1086, 1315, 1494, 1495, 1515, 1517, 1541, 1548, 1573, 1574, 1582, 1585, 1586, 1587, 1589, 1596, 1599, 1614, 1615, 1631, 1660, 1661, 1671, 1700, 1724, 1732, 1797, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 1880, 1927, 1943, 2096, 2100, 2104, 2108, 2149, 2152, 2157, 2158, 2160, 2165, 2170, 2175, 2181, 2202, 2204], "bla": [3, 20, 2132], "choos": [3, 10, 16, 20, 68, 921, 934, 935, 961, 1013, 1066, 1361, 1374, 1379, 1494, 1998, 2128, 2129, 2132, 2152, 2158, 2161, 2175, 2184], "cubla": [3, 12, 18, 20, 21, 1057, 2036, 2148, 2188], "cublaslt": [3, 18, 21], "ck": 3, "rocm": [3, 18, 696, 699, 969, 981, 1085, 1410, 1420, 1509, 1510, 1511, 1512, 1513, 1514, 1534, 1553, 1569, 2096], "subject": [3, 4, 12, 19, 20, 24, 29, 31, 34, 36, 37, 59, 67, 68, 69, 232, 894, 1641, 1642, 1643, 1646, 1740, 1772, 2100, 2119, 2120, 2125, 2130, 2136, 2149, 2155, 2159, 2160, 2161, 2165, 2170, 2172, 2173, 2179, 2180, 2181, 2184, 2190, 2200], "hipbla": [3, 18, 20], "hipblaslt": [3, 18, 20], "offer": [3, 29, 31, 33, 36, 59, 62, 1588, 1626, 1627, 1628, 1629, 1630, 1772, 2036, 2133, 2136, 2142, 2153, 2158, 2159, 2165, 2170, 2189, 2194, 2198, 2207], "wherev": [3, 10, 2119], "prefer": [3, 6, 10, 24, 29, 31, 40, 51, 59, 891, 907, 939, 1219, 1242, 1313, 1356, 1369, 1373, 1381, 1388, 1589, 1740, 1931, 1993, 2014, 2097, 2104, 2108, 2130, 2133, 2147, 2155, 2157, 2158, 2170, 2194, 2203], "environ": [3, 4, 8, 15, 16, 19, 20, 23, 25, 31, 35, 36, 38, 40, 43, 48, 54, 55, 57, 68, 680, 1056, 1227, 1229, 1252, 1325, 1331, 1552, 1598, 2036, 2095, 2096, 2097, 2106, 2130, 2132, 2133, 2135, 2142, 2147, 2148, 2150, 2165, 2183, 2184, 2185, 2187, 2189, 2194, 2203, 2204], "variabl": [3, 4, 6, 13, 15, 19, 20, 23, 25, 33, 35, 36, 38, 40, 43, 48, 50, 54, 56, 57, 59, 64, 68, 70, 73, 78, 79, 447, 680, 722, 723, 724, 725, 726, 727, 728, 729, 730, 733, 734, 735, 736, 738, 739, 740, 741, 742, 744, 748, 749, 750, 751, 752, 753, 755, 756, 766, 770, 774, 813, 843, 844, 846, 929, 930, 934, 1003, 1018, 1022, 1026, 1114, 1146, 1214, 1229, 1329, 1331, 1388, 1499, 1509, 1510, 1511, 1512, 1513, 1514, 1518, 1524, 1525, 1533, 1534, 1552, 1553, 1554, 1567, 1569, 1582, 1585, 1590, 1598, 1600, 1772, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1834, 1838, 1840, 1864, 1865, 1866, 1867, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1878, 1879, 1881, 1904, 1906, 1908, 2036, 2048, 2093, 2095, 2096, 2106, 2107, 2120, 2121, 2130, 2132, 2133, 2135, 2138, 2141, 2142, 2147, 2148, 2150, 2152, 2155, 2157, 2160, 2165, 2172, 2175, 2183, 2184, 2185, 2190, 2191, 2192, 2194, 2196, 2203, 2204], "torch_blas_prefer_cublaslt": 3, "global": [3, 4, 6, 9, 12, 13, 21, 24, 29, 31, 34, 36, 37, 38, 40, 51, 59, 64, 67, 68, 70, 73, 78, 79, 883, 890, 894, 902, 957, 970, 979, 1018, 1037, 1063, 1077, 1095, 1103, 1146, 1148, 1158, 1163, 1181, 1197, 1200, 1203, 1273, 1274, 1296, 1303, 1315, 1318, 1331, 1335, 1386, 1402, 1582, 1740, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1802, 1806, 1834, 1880, 1904, 1906, 1908, 1911, 1935, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2078, 2093, 2097, 2100, 2101, 2104, 2112, 2122, 2129, 2136, 2143, 2144, 2146, 2148, 2160, 2165, 2166, 2167, 2170, 2173, 2175, 2191, 2192, 2193, 2194, 2195, 2202, 2204], "overridden": [3, 6, 15, 68, 918, 919, 920, 934, 935, 1086, 1582, 1971, 2100, 2130, 2136, 2147, 2204, 2205], "achiev": [3, 20, 24, 25, 29, 31, 37, 38, 51, 59, 1145, 1210, 1315, 1495, 1501, 1582, 1588, 1589, 1661, 1690, 1772, 1880, 2114, 2133, 2143, 2146, 2158, 2165, 2167, 2172, 2204], "better": [3, 4, 8, 9, 10, 15, 24, 28, 29, 40, 55, 63, 944, 1001, 1114, 1202, 1206, 1207, 1223, 1302, 1331, 1517, 1616, 1740, 1746, 1772, 1781, 1782, 1875, 1880, 1902, 2100, 2121, 2129, 2130, 2132, 2133, 2139, 2141, 2148, 2150, 2155, 2157, 2159, 2160, 2161, 2170, 2175, 2181, 2184, 2189, 2191, 2192, 2200, 2203], "select": [3, 6, 16, 18, 20, 22, 24, 29, 31, 38, 40, 48, 56, 312, 314, 316, 320, 684, 685, 686, 770, 991, 1028, 1030, 1034, 1045, 1046, 1050, 1058, 1059, 1060, 1061, 1063, 1064, 1072, 1088, 1092, 1094, 1095, 1097, 1099, 1101, 1102, 1103, 1108, 1109, 1110, 1111, 1114, 1115, 1118, 1120, 1122, 1123, 1165, 1388, 1417, 1446, 1447, 1448, 1449, 1450, 1461, 1463, 1465, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1533, 1552, 1598, 1646, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1811, 1812, 1934, 2011, 2039, 2051, 2055, 2056, 2057, 2058, 2060, 2076, 2077, 2078, 2079, 2080, 2081, 2083, 2084, 2087, 2090, 2091, 2097, 2098, 2100, 2118, 2119, 2120, 2121, 2130, 2132, 2133, 2141, 2148, 2151, 2156, 2160, 2170, 2174, 2177, 2192, 2198, 2200, 2203, 2206], "incorrect": [3, 5, 6, 59, 68, 253, 512, 555, 929, 932, 934, 948, 957, 983, 1003, 1013, 1331, 1374, 1387, 1588, 1626, 1627, 1628, 1629, 1630, 1758, 1830, 1982, 1993, 2097, 2100, 2104, 2133, 2147, 2155, 2191, 2207], "_blasbackend": 3, "preferred_rocm_fa_librari": 3, "flash": [3, 1641, 1646], "attent": [3, 8, 36, 37, 745, 1495, 1588, 1626, 1628, 1630, 1641, 1643, 1661, 1740, 2096, 2150, 2174, 2191], "aotriton": 3, "experimeent": 3, "composable_kernel": 3, "torch_rocm_fa_prefer_ck": 3, "_rocmfabackend": 3, "preferred_linalg_librari": [3, 1352], "heurist": [3, 15, 24, 33, 50, 51, 68, 1006, 1145, 2139, 2188, 2197], "cusolv": [3, 1379, 1380, 1997], "magma": [3, 1361, 1405, 1895, 1997, 2150, 2170], "algebra": [3, 10, 1145, 1352, 1373, 2105], "decid": [3, 5, 8, 29, 40, 50, 57, 63, 485, 1225, 1806, 2107, 2155, 2170, 2193, 2194, 2197], "pick": [3, 29, 49, 51, 55, 512, 1001, 2130, 2165, 2190, 2192, 2197], "torch_linalg_prefer_cusolv": 3, "linalg": [3, 12, 405, 991, 992, 993, 994, 1036, 1131, 1259, 1293, 1396, 1405, 1406, 1407, 1411, 1412, 1789, 1790, 1795, 1830, 1882, 1887, 1889, 1895, 1965, 1977, 1997, 1998, 2023, 2045, 2096], "inv": [3, 38, 1293, 1347, 1351, 1357, 1373, 1377, 1381], "inv_ex": [3, 1356], "cholesky_ex": [3, 1345], "lu_factor": [3, 1364, 1365, 1405, 1406, 1407], "lu": [3, 11, 1357, 1363, 1364, 1365, 1406, 1407, 2098], "eigh": [3, 1345, 1351, 1354, 1373, 1379, 2147], "eighval": 3, "svdval": [3, 1347, 1361, 1370, 1379, 1997, 2147], "_linalgbackend": 3, "sdpaparam": 3, "flash_sdp_en": 3, "scale": [3, 8, 24, 35, 38, 40, 49, 51, 96, 172, 472, 474, 616, 695, 699, 700, 701, 748, 749, 750, 751, 752, 753, 754, 759, 760, 761, 762, 763, 764, 765, 766, 769, 780, 782, 783, 784, 785, 787, 790, 791, 807, 813, 816, 834, 836, 837, 838, 839, 840, 845, 853, 969, 1159, 1160, 1163, 1181, 1213, 1214, 1402, 1490, 1519, 1524, 1525, 1526, 1542, 1554, 1610, 1635, 1636, 1637, 1641, 1646, 1679, 1680, 1681, 1697, 1699, 1740, 1741, 1746, 1780, 1840, 1841, 1868, 1897, 1898, 1899, 1900, 1901, 1974, 1975, 1994, 2048, 2096, 2098, 2120, 2121, 2126, 2128, 2133, 2138, 2140, 2144, 2160, 2161, 2163, 2171, 2194], "product": [3, 16, 29, 31, 36, 38, 50, 149, 696, 699, 700, 701, 770, 922, 927, 938, 940, 941, 942, 943, 969, 981, 987, 991, 1022, 1036, 1050, 1103, 1108, 1122, 1123, 1126, 1142, 1145, 1209, 1213, 1214, 1292, 1336, 1348, 1355, 1381, 1382, 1384, 1410, 1417, 1420, 1470, 1533, 1534, 1552, 1553, 1641, 1646, 1687, 1740, 1789, 1883, 1884, 1893, 1939, 2016, 2031, 2045, 2048, 2097, 2119, 2121, 2126, 2130, 2141, 2143, 2158, 2160, 2170, 2191, 2203, 2206], "enable_mem_efficient_sdp": [3, 1740], "mem_efficient_sdp_en": 3, "enable_flash_sdp": [3, 1740], "math_sdp_en": 3, "math": [3, 24, 68, 1155, 1367, 1641, 1646, 1700, 1740, 1885, 1902, 1998, 2097, 2099, 2100, 2115, 2139, 2147, 2170, 2171, 2177, 2185, 2204], "enable_math_sdp": [3, 1740], "fp16_bf16_reduction_math_sdp_allow": 3, "allow_fp16_bf16_reduction_math_sdp": [3, 2147], "cudnn_sdp_en": 3, "enable_cudnn_sdp": 3, "is_flash_attention_avail": 3, "check": [3, 4, 5, 6, 12, 15, 20, 21, 24, 29, 30, 31, 36, 38, 50, 59, 70, 71, 76, 79, 85, 87, 220, 232, 339, 498, 683, 688, 704, 890, 929, 930, 932, 933, 934, 935, 948, 949, 955, 1003, 1014, 1039, 1040, 1044, 1048, 1081, 1101, 1225, 1229, 1237, 1238, 1248, 1249, 1250, 1302, 1312, 1315, 1331, 1332, 1345, 1346, 1351, 1352, 1354, 1357, 1359, 1364, 1370, 1373, 1377, 1379, 1380, 1405, 1432, 1444, 1445, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1582, 1762, 1772, 1792, 1795, 1808, 1818, 1837, 1838, 1880, 1971, 1979, 1980, 1981, 1982, 1983, 1984, 2037, 2053, 2054, 2081, 2095, 2099, 2100, 2101, 2104, 2110, 2119, 2120, 2129, 2133, 2136, 2137, 2138, 2139, 2143, 2144, 2145, 2148, 2149, 2152, 2155, 2157, 2158, 2159, 2160, 2165, 2170, 2172, 2175, 2177, 2187, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2202, 2203, 2204, 2205, 2208], "flashattent": [3, 1630, 1740], "depend": [3, 5, 6, 13, 15, 24, 29, 31, 33, 34, 35, 36, 38, 40, 50, 51, 53, 56, 59, 68, 85, 314, 795, 907, 953, 1050, 1103, 1108, 1122, 1123, 1166, 1168, 1176, 1177, 1178, 1204, 1208, 1213, 1229, 1239, 1241, 1242, 1243, 1246, 1327, 1331, 1351, 1352, 1379, 1405, 1410, 1444, 1494, 1495, 1515, 1517, 1525, 1528, 1541, 1547, 1548, 1573, 1574, 1585, 1586, 1587, 1589, 1596, 1614, 1615, 1631, 1634, 1635, 1653, 1660, 1661, 1671, 1680, 1688, 1700, 1724, 1732, 1740, 1759, 1762, 1772, 1795, 1796, 1802, 1868, 1883, 1885, 1902, 1918, 1979, 1980, 1981, 1983, 1984, 1997, 2023, 2051, 2095, 2097, 2099, 2100, 2104, 2107, 2108, 2118, 2126, 2130, 2132, 2133, 2136, 2137, 2139, 2141, 2144, 2146, 2151, 2154, 2155, 2159, 2160, 2165, 2170, 2176, 2177, 2182, 2184, 2188, 2189, 2190, 2191, 2193, 2194, 2196, 2199, 2201, 2202, 2206], "can_use_flash_attent": 3, "debug": [3, 5, 6, 9, 20, 21, 24, 35, 41, 47, 55, 680, 812, 833, 851, 854, 858, 872, 948, 949, 1001, 1003, 1011, 1037, 1076, 1119, 1263, 1345, 1765, 1766, 1767, 1768, 1938, 1945, 2099, 2106, 2130, 2132, 2133, 2135, 2142, 2143, 2144, 2148, 2150, 2155, 2158, 2180, 2190, 2191, 2192, 2195, 2196, 2199, 2206, 2207, 2208], "util": [3, 8, 18, 31, 51, 55, 56, 57, 59, 60, 67, 68, 70, 498, 742, 745, 748, 749, 750, 755, 756, 766, 770, 774, 802, 811, 935, 1055, 1146, 1147, 1148, 1196, 1238, 1248, 1249, 1250, 1253, 1315, 1533, 1552, 1582, 1598, 1599, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1792, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1816, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1868, 1875, 1880, 2001, 2003, 2005, 2006, 2007, 2036, 2096, 2100, 2104, 2108, 2113, 2121, 2129, 2131, 2132, 2133, 2136, 2138, 2140, 2141, 2143, 2144, 2146, 2148, 2153, 2155, 2158, 2184, 2190, 2191, 2194, 2201, 2204, 2207], "_sdpaparam": 3, "kei": [3, 4, 24, 31, 35, 40, 48, 50, 51, 55, 56, 57, 59, 66, 68, 414, 602, 680, 745, 812, 883, 888, 956, 961, 968, 1111, 1202, 1212, 1230, 1315, 1331, 1332, 1387, 1582, 1583, 1588, 1592, 1626, 1627, 1628, 1629, 1630, 1643, 1740, 1809, 1825, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 1880, 2037, 2039, 2083, 2084, 2098, 2099, 2100, 2104, 2112, 2126, 2133, 2136, 2143, 2144, 2149, 2155, 2156, 2157, 2159, 2160, 2163, 2165, 2166, 2172, 2175, 2177, 2180, 2182, 2195, 2200, 2201, 2202, 2207], "mask": [3, 48, 398, 399, 400, 401, 402, 543, 745, 948, 949, 1409, 1490, 1501, 1526, 1588, 1626, 1627, 1628, 1629, 1630, 1681, 1740, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1809, 1810, 1811, 1812, 1863, 2096, 2098, 2104, 2119, 2120, 2121, 2126, 2170, 2194, 2198], "dropout": [3, 6, 37, 68, 744, 745, 770, 1315, 1490, 1520, 1521, 1522, 1526, 1533, 1552, 1582, 1588, 1598, 1599, 1626, 1628, 1630, 1654, 1674, 1675, 1676, 1681, 1740, 1880, 2098, 2110, 2120, 2130, 2133, 2156, 2162], "causal": [3, 745, 1588, 1626, 1627, 1628, 1629, 1630, 1642, 1643, 1644, 1645, 1740, 2126], "warn": [3, 4, 15, 19, 20, 25, 26, 29, 34, 35, 36, 55, 680, 937, 943, 991, 1050, 1103, 1108, 1119, 1122, 1123, 1214, 1229, 1296, 1312, 1331, 1373, 1387, 1417, 1588, 1626, 1627, 1628, 1629, 1630, 1740, 1772, 1792, 1795, 1938, 1945, 1993, 2036, 2048, 2095, 2106, 2113, 2126, 2131, 2136, 2149, 2152, 2156, 2158, 2164, 2191, 2203, 2204], "why": [3, 4, 8, 24, 55, 64, 68, 1145, 1319, 1740, 1843, 1844, 1862, 2104, 2107, 2137, 2139, 2190, 2191, 2193, 2201, 2206], "could": [3, 5, 6, 8, 9, 18, 20, 24, 29, 35, 36, 37, 38, 40, 50, 57, 64, 67, 68, 582, 583, 584, 586, 587, 812, 957, 1081, 1166, 1168, 1176, 1177, 1178, 1230, 1312, 1315, 1321, 1323, 1346, 1387, 1405, 1434, 1435, 1582, 1781, 1782, 1867, 1880, 1973, 1998, 2032, 2099, 2100, 2107, 2118, 2130, 2133, 2135, 2149, 2150, 2153, 2155, 2158, 2160, 2161, 2165, 2166, 2167, 2170, 2174, 2175, 2189, 2191, 2194, 2195, 2199, 2203, 2204, 2207, 2208], "can_use_efficient_attent": 3, "efficient_attent": [3, 1641, 1646], "can_use_cudnn_attent": 3, "cudnn_attent": [3, 1641], "sdp_kernel": 3, "enable_flash": 3, "enable_math": 3, "enable_mem_effici": 3, "enable_cudnn": 3, "temporarili": [3, 40, 1935, 2104, 2130, 2155, 2159, 2197], "previou": [3, 19, 29, 31, 33, 50, 55, 57, 59, 68, 512, 555, 770, 1001, 1003, 1042, 1077, 1190, 1269, 1326, 1386, 1402, 1533, 1552, 1598, 1646, 1772, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1837, 1877, 1902, 1993, 2110, 2130, 2133, 2136, 2144, 2150, 2152, 2153, 2157, 2160, 2167, 2194, 2203], "restor": [3, 6, 31, 34, 36, 68, 86, 1012, 1190, 1386, 1402, 1646, 1827, 1922, 1943, 2144, 2149, 2192], "is_avail": [3, 18, 29, 683, 692, 2039, 2133, 2140, 2142, 2145, 2149, 2159, 2184, 2211], "determinist": [3, 4, 6, 23, 29, 33, 38, 68, 86, 485, 498, 512, 902, 1146, 1147, 1148, 1263, 1296, 1318, 1331, 1332, 1413, 1416, 1418, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1552, 1598, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1932, 1938, 2036, 2096, 2098, 2130, 2139, 2148, 2157, 2164], "algorithm": [3, 4, 8, 12, 20, 22, 25, 30, 34, 38, 40, 59, 86, 680, 770, 789, 795, 991, 1312, 1356, 1363, 1369, 1373, 1379, 1388, 1405, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1531, 1533, 1552, 1598, 1634, 1635, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1688, 1699, 1740, 1759, 1772, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1859, 1860, 1861, 1863, 1885, 1924, 1939, 1997, 1998, 2036, 2096, 2130, 2133, 2147, 2165, 2170], "are_deterministic_algorithms_en": [3, 2188], "use_deterministic_algorithm": [3, 28, 498, 902, 1146, 1147, 1148, 1296, 1938, 2148], "benchmark": [3, 20, 680, 2096, 2098, 2133, 2142, 2159, 2188, 2194, 2200, 2203], "fastest": [3, 18, 20, 906, 1772, 1829, 1843, 1844, 1862, 2141, 2148, 2157], "benchmark_limit": 3, "maximum": [3, 28, 38, 50, 51, 55, 498, 705, 707, 781, 807, 838, 839, 840, 845, 850, 903, 1052, 1092, 1094, 1101, 1109, 1110, 1124, 1125, 1146, 1147, 1148, 1191, 1208, 1276, 1277, 1278, 1388, 1413, 1439, 1540, 1679, 1680, 1779, 1841, 1866, 1875, 1902, 1916, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2038, 2076, 2077, 2081, 2098, 2106, 2115, 2121, 2128, 2129, 2133, 2146, 2156, 2163, 2177, 2178, 2190, 2198, 2206, 2208], "try": [3, 4, 5, 8, 9, 29, 31, 36, 37, 42, 43, 47, 50, 55, 63, 64, 232, 1001, 1003, 1115, 1199, 1208, 1214, 1229, 1254, 1320, 1322, 1331, 1352, 1439, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1626, 1627, 1629, 1635, 1641, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1772, 1839, 1840, 1842, 1843, 1844, 1845, 1846, 1847, 1859, 1860, 1861, 1862, 2048, 2095, 2097, 2100, 2101, 2104, 2130, 2133, 2136, 2138, 2141, 2146, 2155, 2157, 2158, 2160, 2161, 2165, 2170, 2189, 2190, 2193, 2194, 2196, 2203, 2204, 2206], "dispatch": [3, 15, 29, 55, 59, 68, 1215, 1216, 1230, 1320, 1434, 1435, 2037, 2039, 2100, 2104, 2133, 2136, 2152, 2155, 2194, 2195, 2205], "v8": [3, 21], "api": [3, 4, 6, 9, 10, 15, 21, 25, 29, 30, 31, 33, 37, 40, 42, 43, 47, 48, 49, 50, 51, 53, 59, 60, 63, 67, 69, 70, 75, 79, 80, 81, 232, 814, 842, 890, 924, 925, 926, 927, 928, 940, 945, 946, 990, 1003, 1006, 1013, 1037, 1056, 1077, 1078, 1079, 1086, 1087, 1089, 1118, 1149, 1189, 1203, 1206, 1207, 1209, 1214, 1227, 1239, 1326, 1327, 1329, 1439, 1463, 1772, 1825, 1827, 1828, 1836, 1924, 1935, 2029, 2039, 2048, 2070, 2090, 2095, 2106, 2107, 2108, 2114, 2115, 2118, 2119, 2121, 2125, 2130, 2135, 2140, 2149, 2151, 2161, 2165, 2166, 2170, 2173, 2175, 2179, 2182, 2184, 2185, 2192, 2196, 2203, 2205, 2208], "get_fastpath_en": 3, "fast": [3, 8, 9, 24, 29, 40, 68, 948, 1523, 1544, 1545, 1546, 1614, 1630, 1781, 1782, 1875, 1883, 1902, 1924, 1939, 2115, 2132, 2133, 2136, 2142, 2157, 2165, 2170, 2173, 2174, 2182, 2191, 2193, 2196, 2197, 2206], "path": [3, 4, 5, 9, 15, 21, 29, 31, 40, 48, 50, 53, 54, 55, 57, 59, 68, 744, 958, 959, 960, 1037, 1145, 1238, 1315, 1582, 1599, 1630, 1837, 1880, 2095, 2098, 2099, 2130, 2133, 2136, 2139, 2142, 2149, 2152, 2155, 2157, 2158, 2159, 2170, 2180, 2184, 2188, 2196, 2197], "transformerencod": 3, "multiheadattent": [3, 1626, 1628, 1630, 2160], "fastpath": [3, 1588, 2193], "condit": [3, 6, 13, 22, 24, 55, 56, 57, 64, 68, 70, 78, 79, 616, 619, 624, 704, 770, 938, 948, 949, 1013, 1018, 1312, 1331, 1345, 1347, 1352, 1355, 1361, 1362, 1363, 1374, 1379, 1533, 1552, 1598, 1630, 1818, 1823, 2023, 2051, 2097, 2098, 2099, 2101, 2109, 2130, 2136, 2147, 2175, 2190, 2191, 2192, 2193, 2198, 2203], "met": [3, 13, 616, 1018, 1355, 1362, 1363, 1374, 1388, 1630, 1982], "set_fastpath_en": 3, "verbos": [3, 15, 20, 29, 68, 680, 1229, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 2095, 2106, 2115, 2120, 2155, 2175, 2204], "On": [3, 15, 23, 24, 29, 30, 33, 38, 51, 57, 59, 64, 68, 696, 699, 969, 981, 1042, 1096, 1363, 1410, 1420, 1509, 1510, 1511, 1512, 1513, 1514, 1534, 1549, 1550, 1551, 1552, 1553, 1569, 1592, 1598, 1611, 1772, 1843, 1844, 1859, 1862, 2097, 2107, 2130, 2132, 2133, 2136, 2147, 2158, 2165, 2166, 2167, 2170, 2188, 2190, 2191, 2193, 2194, 2203], "demand": [3, 24, 1079, 2099, 2121, 2143, 2146, 2165], "onemkl": 3, "easier": [3, 8, 24, 36, 55, 68, 2097, 2099, 2126, 2130, 2131, 2136, 2137, 2139, 2170, 2194, 2203, 2204], "dump": [3, 68, 1037, 2135, 2150, 2155, 2194, 2204, 2208], "durat": [3, 20, 29, 47, 50, 966, 1434, 1435, 2113, 2152, 2159, 2194], "kernel": [3, 4, 5, 12, 15, 19, 21, 29, 59, 67, 87, 485, 680, 692, 749, 750, 751, 752, 753, 782, 783, 784, 955, 1001, 1035, 1040, 1044, 1045, 1086, 1087, 1121, 1123, 1441, 1445, 1446, 1491, 1492, 1493, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1529, 1530, 1549, 1550, 1551, 1558, 1559, 1560, 1561, 1562, 1563, 1575, 1576, 1577, 1578, 1579, 1580, 1634, 1663, 1664, 1665, 1666, 1667, 1668, 1683, 1684, 1740, 1781, 1782, 1793, 2037, 2039, 2054, 2055, 2092, 2097, 2104, 2110, 2115, 2121, 2126, 2133, 2136, 2137, 2139, 2145, 2147, 2155, 2157, 2159, 2161, 2170, 2185, 2188, 2191, 2192, 2193, 2194, 2195, 2196, 2203, 2204, 2207], "mkl_verbos": 3, "methodologi": 3, "larg": [3, 4, 8, 9, 24, 29, 35, 37, 68, 680, 887, 1024, 1026, 1044, 1101, 1352, 1361, 1379, 1486, 1528, 1634, 1682, 1772, 1875, 1963, 1976, 1998, 2054, 2081, 2096, 2118, 2120, 2121, 2132, 2133, 2136, 2138, 2144, 2147, 2149, 2152, 2155, 2158, 2161, 2165, 2170, 2173, 2176, 2188, 2194, 2201, 2203, 2204, 2206], "moreov": [3, 31, 57, 512, 1772, 1862, 2207], "investig": [3, 8, 29, 64, 2201, 2203], "singl": [3, 4, 6, 15, 20, 25, 29, 31, 34, 35, 36, 37, 38, 40, 43, 48, 49, 50, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 78, 79, 253, 680, 705, 706, 776, 777, 778, 779, 782, 783, 784, 828, 924, 937, 938, 939, 940, 941, 942, 943, 955, 970, 979, 983, 1001, 1003, 1022, 1026, 1086, 1089, 1202, 1204, 1205, 1208, 1210, 1214, 1273, 1274, 1278, 1300, 1314, 1315, 1331, 1332, 1335, 1393, 1417, 1434, 1435, 1484, 1485, 1488, 1489, 1492, 1493, 1495, 1509, 1510, 1511, 1513, 1514, 1517, 1518, 1529, 1530, 1536, 1549, 1550, 1551, 1554, 1576, 1577, 1582, 1590, 1594, 1595, 1597, 1611, 1622, 1647, 1648, 1649, 1650, 1651, 1652, 1655, 1656, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1683, 1684, 1685, 1713, 1714, 1715, 1758, 1764, 1766, 1769, 1770, 1771, 1772, 1778, 1779, 1780, 1783, 1785, 1787, 1788, 1825, 1826, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1857, 1859, 1860, 1861, 1862, 1863, 1868, 1880, 1892, 1918, 1939, 1985, 1991, 1992, 2032, 2033, 2036, 2043, 2044, 2048, 2097, 2099, 2100, 2107, 2110, 2118, 2120, 2121, 2122, 2126, 2130, 2132, 2133, 2136, 2137, 2139, 2141, 2144, 2146, 2147, 2148, 2149, 2155, 2157, 2158, 2159, 2160, 2166, 2167, 2170, 2172, 2173, 2176, 2177, 2181, 2187, 2188, 2190, 2192, 2193, 2194, 2196, 2202, 2203, 2204], "enough": [3, 9, 25, 36, 57, 68, 1229, 1236, 1327, 1632, 1979, 1980, 1981, 1982, 1983, 1984, 2036, 2099, 2118, 2121, 2133, 2136, 2141, 2157, 2158, 2173, 2191, 2194, 2203, 2205], "scope": [3, 8, 13, 53, 68, 833, 1018, 1105, 1199, 1622, 1806, 2097, 2099, 2100, 2133, 2138, 2155, 2158, 2167, 2193, 2202], "second": [3, 6, 15, 19, 29, 34, 35, 40, 43, 50, 53, 55, 65, 68, 696, 699, 701, 704, 770, 806, 903, 904, 905, 913, 921, 934, 935, 948, 949, 969, 973, 974, 976, 977, 978, 981, 1036, 1050, 1103, 1108, 1122, 1123, 1133, 1135, 1136, 1142, 1150, 1191, 1192, 1204, 1207, 1208, 1209, 1213, 1214, 1242, 1257, 1258, 1269, 1272, 1280, 1281, 1282, 1292, 1304, 1338, 1340, 1348, 1384, 1387, 1393, 1394, 1404, 1410, 1414, 1416, 1417, 1419, 1420, 1474, 1479, 1482, 1486, 1492, 1493, 1499, 1510, 1511, 1513, 1514, 1524, 1525, 1532, 1533, 1547, 1550, 1551, 1552, 1570, 1574, 1576, 1577, 1598, 1611, 1670, 1672, 1706, 1762, 1806, 1841, 1843, 1844, 1847, 1859, 1863, 1875, 1877, 1880, 1923, 1973, 1982, 2020, 2025, 2027, 2042, 2045, 2048, 2099, 2100, 2104, 2109, 2118, 2130, 2133, 2136, 2137, 2138, 2139, 2141, 2144, 2148, 2152, 2155, 2157, 2159, 2165, 2171, 2175, 2184, 2188, 2191, 2203], "verbose_on": 3, "level": [3, 4, 8, 9, 10, 13, 16, 19, 24, 26, 29, 31, 33, 35, 36, 40, 42, 43, 47, 55, 56, 57, 59, 63, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 498, 680, 757, 924, 925, 926, 927, 928, 940, 1203, 1223, 1239, 1242, 1259, 1495, 1518, 1772, 1877, 2041, 2097, 2100, 2101, 2106, 2113, 2115, 2118, 2128, 2130, 2132, 2136, 2139, 2141, 2152, 2158, 2159, 2160, 2161, 2165, 2170, 2172, 2175, 2180, 2181, 2182, 2183, 2185, 2190, 2191, 2192, 2194, 2195, 2198, 2201, 2202, 2203, 2204, 2205], "verbose_off": 3, "dnn": [3, 2132], "onednn": [3, 884, 885, 888, 1317, 1324, 2156, 2160], "former": [3, 57, 59, 1552, 1582, 2130, 2152], "dnnl_verbos": 3, "verbose_on_cr": 3, "set_flag": 3, "_enabl": 3, "opt": [3, 29, 34, 1145, 1327, 2097, 2141, 2203], "einsum": [3, 2098, 2156], "automat": [3, 15, 20, 25, 29, 31, 50, 51, 55, 68, 588, 918, 920, 934, 935, 955, 1001, 1044, 1079, 1089, 1145, 1278, 1325, 1331, 1417, 1629, 1740, 1773, 1774, 1914, 1943, 1996, 2037, 2054, 2095, 2096, 2099, 2100, 2109, 2110, 2111, 2119, 2120, 2130, 2131, 2133, 2136, 2140, 2144, 2146, 2155, 2158, 2160, 2161, 2165, 2173, 2175, 2176, 2183, 2190, 2193, 2194, 2196, 2201, 2203, 2204], "pip": [3, 1145, 2150, 2152, 2153, 2154, 2155, 2175, 2192, 2203], "itself": [3, 4, 6, 8, 9, 20, 29, 30, 33, 36, 55, 59, 65, 68, 487, 703, 709, 908, 1026, 1145, 1207, 1208, 1315, 1327, 1331, 1582, 1616, 1746, 1772, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1970, 2097, 2118, 2136, 2144, 2146, 2148, 2151, 2155, 2158, 2165, 2170, 2172, 2174, 2179, 2189, 2191, 2193, 2204], "accordingli": [3, 38, 68, 1325, 1552, 1795, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 2157, 2165, 2199], "properli": [3, 4, 8, 24, 25, 31, 34, 36, 40, 50, 67, 1003, 1166, 1167, 1168, 1176, 1177, 1178, 1417, 1583, 1584, 1592, 1593, 1772, 2037, 2095, 2107, 2121, 2136, 2141, 2144, 2146, 2149, 2165, 2166, 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1706, 1709, 1710, 1711, 1713, 1714, 1715, 1993, 2096, 2118, 2146, 2159, 2165, 2208], "form": [4, 8, 10, 12, 24, 29, 35, 38, 49, 50, 51, 55, 56, 57, 59, 64, 68, 770, 789, 795, 992, 1133, 1173, 1175, 1222, 1225, 1315, 1355, 1358, 1363, 1374, 1379, 1484, 1485, 1488, 1489, 1501, 1529, 1530, 1533, 1552, 1582, 1598, 1634, 1635, 1672, 1683, 1684, 1687, 1699, 1727, 1740, 1759, 1795, 1862, 1864, 1870, 1877, 1878, 1880, 1883, 2016, 2095, 2097, 2101, 2121, 2130, 2136, 2144, 2149, 2152, 2155, 2158, 2160, 2170, 2175, 2191, 2192, 2196, 2202, 2203], "treat": [4, 38, 49, 55, 57, 62, 68, 70, 75, 76, 320, 470, 805, 948, 949, 1003, 1086, 1202, 1243, 1315, 1368, 1371, 1373, 1385, 1387, 1397, 1398, 1399, 1400, 1417, 1476, 1525, 1535, 1554, 1586, 1587, 1588, 1589, 1592, 1597, 1611, 1616, 1680, 1771, 1772, 1825, 1829, 1868, 1924, 1939, 1993, 2010, 2011, 2017, 2032, 2099, 2100, 2104, 2119, 2121, 2130, 2152, 2165, 2170, 2173, 2181, 2191, 2194, 2195, 2199, 2202, 2203], "distinct": [4, 12, 1202, 1351, 1352, 1396, 1495, 2069, 2100, 2136, 2149, 2157, 2165, 2166, 2190, 2203], "workload": [4, 9, 20, 24, 29, 59, 1089, 2133, 2139, 2143, 2147, 2159, 2165, 2183, 2187, 2201], "good": [4, 8, 9, 15, 68, 1001, 1206, 1236, 1590, 1877, 1959, 2095, 2104, 2118, 2133, 2136, 2139, 2143, 2144, 2146, 2155, 2158, 2160, 2190, 2191, 2192, 2193, 2200, 2203], "intrins": [4, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 804, 805, 2161, 2162], "contrast": [4, 38, 40, 55, 57, 770, 1225, 1533, 1841, 1862, 2128, 2130, 2137, 2139, 2170, 2198], "adaptive_autorang": 4, "threshold": [4, 25, 68, 1370, 1373, 1542, 1614, 1619, 1697, 1748, 1754, 1841, 1877, 1943, 2098, 2129, 2133, 2156, 2175], "min_run_tim": 4, "01": [4, 30, 34, 53, 487, 765, 790, 839, 840, 1146, 1188, 1568, 1703, 1704, 1762, 1837, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1868, 1875, 1897, 1950, 1952, 1953, 1958, 1959, 2098, 2128, 2143, 2155, 2157, 2198, 2203], "max_run_tim": 4, "callback": [4, 25, 29, 40, 53, 67, 68, 1772, 2113, 2136, 2143, 2159, 2165], "similar": [4, 8, 10, 12, 21, 24, 29, 33, 35, 36, 38, 47, 56, 59, 64, 67, 68, 492, 514, 722, 723, 724, 725, 726, 727, 730, 740, 741, 742, 743, 755, 756, 766, 774, 816, 906, 921, 934, 935, 982, 987, 1000, 1001, 1191, 1192, 1315, 1515, 1516, 1528, 1541, 1544, 1545, 1546, 1582, 1631, 1670, 1816, 1830, 1831, 1841, 1863, 1880, 1889, 1917, 1924, 1973, 1977, 2011, 2017, 2033, 2099, 2100, 2104, 2118, 2119, 2121, 2130, 2132, 2133, 2136, 2137, 2141, 2147, 2158, 2160, 2165, 2166, 2170, 2171, 2172, 2176, 2190, 2193, 2194, 2196, 2199, 2203, 2204, 2209], "blocked_autorang": 4, "variablil": 4, "until": [4, 6, 8, 24, 25, 29, 31, 34, 40, 50, 55, 59, 67, 68, 85, 87, 485, 1039, 1040, 1044, 1079, 1199, 1336, 1388, 1428, 1434, 1435, 1444, 1445, 1688, 1762, 1772, 1865, 1867, 1872, 2017, 2053, 2054, 2070, 2104, 2112, 2118, 2128, 2133, 2138, 2139, 2149, 2155, 2159, 2165, 2167, 2172, 2188, 2191, 2194], "iqr": 4, "smaller": [4, 20, 24, 33, 68, 498, 544, 1026, 1552, 1772, 1877, 1894, 1939, 1985, 2133, 2149, 2157, 2158, 2189, 2206, 2209], "reach": [4, 8, 9, 10, 24, 25, 29, 30, 40, 50, 55, 60, 1388, 1772, 1865, 1868, 1872, 1873, 2130, 2136, 2146, 2161, 2165, 2192], "At": [4, 6, 7, 8, 16, 18, 20, 24, 33, 35, 1179, 1496, 1497, 1498, 1509, 1510, 1511, 1512, 1513, 1514, 1549, 1550, 1551, 2037, 2041, 2110, 2132, 2141, 2160, 2165, 2171, 2191, 2192, 2194, 2204], "high": [4, 5, 8, 9, 10, 11, 16, 25, 29, 33, 38, 40, 47, 49, 51, 68, 118, 1501, 1629, 1906, 1907, 1939, 2041, 2098, 2108, 2113, 2115, 2118, 2136, 2141, 2142, 2144, 2145, 2146, 2157, 2160, 2161, 2165, 2170, 2175, 2176, 2177, 2181, 2185, 2187, 2188, 2191, 2198, 2203, 2204, 2207, 2208], "pseudo": [4, 86], "block_siz": [4, 834, 844, 847, 2104, 2126], "enough_data": 4, "len": [4, 24, 31, 68, 70, 75, 217, 544, 703, 705, 706, 709, 1164, 1168, 1171, 1175, 1178, 1182, 1213, 1319, 1382, 1403, 1415, 1473, 1476, 1518, 1727, 1801, 1803, 1819, 1867, 1875, 1982, 1985, 1991, 1992, 1996, 2042, 2043, 2044, 2097, 2098, 2100, 2136, 2140, 2156, 2157, 2169, 2170, 2175, 2191, 2202, 2203], "small_iqr": 4, "break": [4, 8, 29, 38, 62, 68, 680, 922, 1001, 1003, 1229, 1315, 1361, 1582, 1880, 1924, 1943, 2096, 2101, 2106, 2108, 2121, 2135, 2157, 2170, 2179, 2184, 2188, 2190, 2192, 2195, 2197, 2199], "stop": [4, 6, 29, 38, 40, 50, 51, 53, 56, 894, 923, 966, 1081, 1229, 1313, 1388, 1501, 1816, 1877, 2097, 2100, 2159, 2165, 2169, 2191, 2195, 2203], "repetit": [4, 1917, 2017], "statist": [4, 25, 29, 38, 813, 814, 838, 839, 840, 842, 845, 1050, 1063, 1092, 1094, 1095, 1097, 1099, 1101, 1102, 1103, 1108, 1109, 1110, 1111, 1122, 1123, 1393, 1453, 1458, 1459, 1460, 1496, 1497, 1498, 1536, 1544, 1545, 1546, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1622, 1880, 2076, 2077, 2078, 2079, 2080, 2081, 2083, 2084, 2113, 2130, 2138, 2157, 2160, 2163, 2194], "minimum": [4, 15, 20, 25, 50, 55, 706, 707, 781, 807, 838, 839, 840, 845, 850, 904, 972, 1125, 1192, 1276, 1277, 1278, 1418, 1501, 1540, 1632, 1866, 1867, 1875, 1959, 1979, 1980, 1981, 1982, 1983, 1984, 2098, 2121, 2128, 2130, 2141, 2156, 2163, 2173, 2198], "total_tim": 4, "choic": [4, 9, 10, 15, 29, 33, 1331, 1379, 1583, 1592, 1902, 2132, 2155, 2163, 2170, 2190, 2191, 2204], "block": [4, 8, 9, 21, 24, 29, 31, 33, 34, 37, 50, 53, 59, 67, 68, 87, 485, 582, 583, 584, 844, 862, 964, 980, 1039, 1044, 1101, 1336, 1388, 1445, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1558, 1559, 1560, 1561, 1562, 1563, 1585, 1588, 1626, 1627, 1628, 1629, 1630, 1634, 1682, 1758, 1772, 1979, 1980, 1981, 2054, 2097, 2099, 2100, 2113, 2118, 2121, 2122, 2126, 2130, 2133, 2135, 2139, 2141, 2155, 2160, 2165, 2170, 2182, 2201, 2203, 2206, 2208], "qualiti": [4, 8, 25], "balanc": [4, 1001], "compet": [4, 2146], "amort": 4, "invoc": [4, 6, 68, 870, 1001, 1006, 1227, 1318, 1331, 2018, 2097, 2100, 2133, 2136, 2143, 2165, 2167, 2183, 2188, 2201, 2203], "less": [4, 7, 8, 15, 20, 24, 25, 29, 36, 38, 51, 53, 55, 59, 363, 680, 837, 937, 939, 948, 949, 1026, 1097, 1115, 1145, 1189, 1193, 1202, 1340, 1352, 1388, 1404, 1405, 1486, 1542, 1596, 1605, 1614, 1620, 1819, 1915, 2025, 2027, 2079, 2095, 2098, 2100, 2121, 2133, 2136, 2139, 2146, 2147, 2156, 2160, 2191, 2194, 2201], "bias": [4, 25, 744, 770, 806, 1496, 1497, 1498, 1533, 1534, 1536, 1544, 1545, 1546, 1552, 1553, 1554, 1598, 1600, 1622, 1642, 1643, 2157], "trivial": [4, 33, 36, 40, 43, 57, 745, 991, 1222, 1229, 1789, 1982, 2155, 2167, 2191, 2193], "low": [4, 8, 19, 25, 33, 38, 59, 498, 940, 1239, 1259, 1501, 1885, 1902, 1906, 1907, 1924, 1998, 2098, 2113, 2115, 2118, 2133, 2146, 2147, 2159, 2172, 2177, 2191, 2197, 2198, 2205], "digit": [4, 985, 1943, 2095, 2111, 2143, 2147], "microsecond": [4, 2133], "bia": [4, 10, 29, 35, 55, 70, 722, 723, 724, 725, 726, 727, 728, 729, 730, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 748, 749, 750, 751, 752, 753, 759, 761, 762, 763, 764, 766, 770, 771, 774, 775, 782, 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2161, 2197], "main": [4, 8, 24, 25, 29, 30, 31, 36, 38, 40, 41, 42, 43, 48, 50, 51, 53, 54, 55, 68, 259, 495, 834, 1001, 1132, 1133, 1134, 1135, 1136, 1378, 1690, 1976, 2024, 2025, 2026, 2027, 2095, 2097, 2113, 2118, 2120, 2121, 2129, 2130, 2133, 2135, 2136, 2137, 2139, 2144, 2146, 2150, 2151, 2152, 2160, 2165, 2166, 2175, 2176, 2179, 2182, 2184, 2189, 2191, 2194, 2202, 2203], "collect_callgrind": 4, "collect_baselin": 4, "retain_out_fil": 4, "callgrindstat": 4, "tupl": [4, 6, 13, 15, 20, 24, 29, 31, 35, 36, 37, 40, 47, 48, 50, 55, 56, 57, 59, 68, 232, 318, 444, 446, 448, 492, 496, 519, 536, 559, 582, 583, 584, 703, 705, 706, 707, 709, 745, 776, 777, 778, 779, 782, 783, 784, 789, 795, 796, 797, 805, 809, 812, 831, 832, 834, 844, 862, 883, 890, 891, 907, 908, 915, 916, 917, 918, 919, 921, 934, 935, 937, 938, 939, 940, 941, 942, 943, 948, 949, 952, 953, 954, 968, 984, 995, 1003, 1011, 1018, 1025, 1041, 1051, 1052, 1055, 1068, 1089, 1095, 1124, 1125, 1143, 1146, 1148, 1162, 1164, 1165, 1167, 1168, 1170, 1171, 1172, 1174, 1175, 1177, 1178, 1180, 1182, 1185, 1195, 1200, 1202, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1212, 1213, 1214, 1227, 1229, 1238, 1253, 1255, 1259, 1277, 1279, 1315, 1322, 1327, 1331, 1332, 1337, 1346, 1351, 1352, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1368, 1372, 1374, 1375, 1377, 1379, 1382, 1385, 1403, 1405, 1407, 1413, 1415, 1417, 1418, 1421, 1423, 1453, 1473, 1476, 1483, 1484, 1485, 1487, 1488, 1489, 1491, 1492, 1493, 1501, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1518, 1528, 1529, 1530, 1549, 1550, 1551, 1558, 1559, 1560, 1561, 1562, 1563, 1575, 1576, 1577, 1578, 1579, 1580, 1582, 1583, 1588, 1592, 1604, 1605, 1606, 1607, 1608, 1609, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1648, 1649, 1651, 1652, 1655, 1656, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1683, 1684, 1699, 1713, 1714, 1715, 1725, 1727, 1759, 1760, 1761, 1766, 1772, 1784, 1786, 1795, 1806, 1819, 1825, 1829, 1830, 1834, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 1885, 1886, 1895, 1904, 1906, 1908, 1918, 1922, 1923, 1968, 1976, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1988, 1991, 1992, 1996, 1997, 1998, 2014, 2015, 2016, 2017, 2018, 2023, 2030, 2031, 2032, 2033, 2034, 2043, 2044, 2048, 2049, 2051, 2093, 2097, 2098, 2101, 2104, 2118, 2121, 2126, 2130, 2133, 2136, 2137, 2144, 2149, 2152, 2155, 2157, 2158, 2160, 2165, 2169, 2170, 2172, 2175, 2177, 2179, 2181, 2191, 2194, 2202, 2205], "valgrind_wrapp": 4, "timer_interfac": 4, "modulo": [4, 38, 1193, 1242, 1915], "determin": [4, 6, 9, 12, 15, 18, 19, 23, 24, 25, 29, 38, 40, 48, 50, 59, 64, 68, 232, 841, 850, 937, 939, 948, 949, 970, 979, 986, 991, 1090, 1147, 1166, 1176, 1201, 1202, 1231, 1232, 1273, 1274, 1277, 1278, 1329, 1349, 1361, 1372, 1375, 1396, 1410, 1525, 1529, 1530, 1552, 1588, 1591, 1598, 1641, 1680, 1683, 1684, 1699, 1733, 1759, 1794, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1825, 1835, 1875, 1905, 1907, 1909, 1936, 1979, 1980, 1981, 1982, 1983, 1984, 1993, 2074, 2094, 2100, 2101, 2120, 2121, 2133, 2135, 2136, 2139, 2144, 2152, 2155, 2158, 2160, 2165, 2167, 2170, 2172, 2173, 2175, 2177, 2190, 2193, 2194, 2202, 2203, 2204, 2211], "jitter": 4, "interpret": [4, 24, 29, 34, 38, 40, 50, 53, 55, 56, 57, 232, 789, 795, 909, 943, 1041, 1100, 1166, 1167, 1168, 1173, 1175, 1176, 1177, 1178, 1199, 1227, 1266, 1278, 1315, 1320, 1327, 1331, 1368, 1388, 1521, 1646, 1688, 1699, 1700, 1759, 1936, 1941, 2095, 2099, 2100, 2104, 2118, 2132, 2133, 2170, 2172, 2174, 2191, 2192, 2203], "ideal": [4, 49, 51, 1331, 1863, 2120, 2121, 2194, 2203], "analysi": [4, 25, 38, 55, 57, 68, 1335, 1885, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2101, 2108, 2144, 2151, 2152, 2181, 2189, 2191, 2192, 2193, 2197], "valgrind": 4, "degrad": [4, 15, 59, 2132, 2135, 2136, 2170], "due": [4, 5, 6, 8, 25, 29, 31, 33, 38, 49, 59, 64, 68, 70, 72, 485, 909, 937, 938, 943, 1022, 1219, 1328, 1351, 1352, 1379, 1405, 1521, 1535, 1626, 1635, 1699, 1740, 1823, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 1998, 2097, 2133, 2141, 2142, 2147, 2148, 2149, 2155, 2160, 2161, 2165, 2167, 2188, 2194, 2199, 2203, 2204, 2206, 2208], "amelior": 4, "suffici": [4, 15, 25, 29, 38, 42, 50, 51, 56, 59, 485, 1013, 1843, 1844, 1862, 1998, 2095, 2104, 2140, 2170, 2173, 2194], "callgrind_control": 4, "callgrind_annot": 4, "boundari": [4, 33, 35, 68, 789, 795, 985, 1269, 1503, 1504, 1505, 1506, 1507, 1508, 1604, 1605, 1606, 1607, 1608, 1609, 1635, 1638, 1639, 1640, 1699, 1759, 1868, 1875, 2098, 2121, 2135, 2158, 2165, 2194], "caller": [4, 40, 50, 68, 883, 1315, 1582, 1630, 1880, 1982, 2104, 2130, 2133, 2165, 2167], "structur": [4, 6, 10, 13, 24, 25, 29, 33, 35, 39, 40, 43, 51, 55, 56, 57, 59, 68, 70, 71, 75, 79, 921, 934, 935, 967, 1001, 1018, 1196, 1209, 1214, 1227, 1234, 1235, 1331, 1582, 1642, 1802, 1806, 1838, 1885, 1998, 2048, 2099, 2101, 2104, 2107, 2113, 2126, 2133, 2135, 2136, 2137, 2139, 2146, 2149, 2150, 2155, 2157, 2158, 2165, 2172, 2175, 2176, 2177, 2179, 2181, 2184, 2190, 2191, 2192, 2194, 2195, 2199, 2206], "restrict": [4, 9, 13, 24, 38, 56, 59, 64, 65, 1003, 1018, 1387, 1410, 1517, 2099, 2100, 2101, 2120, 2121, 2130, 2133, 2141, 2149, 2160, 2190], "builtin": [4, 29, 68, 70, 79, 1328, 1387, 1911, 2005, 2097, 2099, 2101, 2165, 2167, 2191, 2194, 2203, 2204], "surpris": [4, 9, 59, 2095, 2141, 2147, 2193], "serial": [4, 16, 24, 29, 31, 34, 50, 1315, 1325, 1326, 1387, 1582, 1762, 1880, 1927, 2095, 2096, 2098, 2111, 2120, 2130, 2133, 2139, 2143, 2144, 2146, 2152, 2155, 2158, 2160, 2165, 2166, 2172], "subsequ": [4, 8, 15, 16, 18, 29, 55, 57, 59, 68, 1001, 1040, 1075, 1315, 1327, 1331, 1509, 1510, 1511, 1512, 1513, 1514, 1582, 1611, 1880, 2121, 2133, 2148, 2155, 2159, 2165, 2170, 2183, 2184, 2188, 2194], "deseri": [4, 31, 1229, 1387, 1762, 2095, 2111, 2149, 2165, 2172], "globalsbridg": 4, "care": [4, 8, 15, 29, 38, 59, 67, 68, 1166, 1168, 1228, 1229, 1582, 2118, 2132, 2133, 2135, 2136, 2138, 2144, 2146, 2149, 2155, 2165, 2170, 2188, 2193, 2194, 2195], "reli": [4, 10, 15, 24, 25, 35, 40, 43, 59, 68, 149, 922, 948, 1145, 1518, 1863, 2109, 2130, 2132, 2135, 2136, 2139, 2148, 2149, 2170, 2174, 2193, 2194, 2195, 2197], "pickl": [4, 24, 25, 29, 1315, 1323, 1387, 1582, 1880, 1927, 2095, 2111, 2118, 2149, 2153, 2158, 2165, 2206], "transfer": [4, 24, 29, 59, 2110, 2118, 2133, 2142, 2144, 2157, 2158, 2165, 2173], "profil": [4, 5, 18, 20, 47, 68, 485, 956, 957, 964, 1001, 1056, 1329, 1460, 1765, 1766, 1767, 1768, 1943, 2096, 2115, 2139, 2155, 2156, 2165, 2182, 2183, 2194, 2203], "empti": [4, 13, 28, 29, 31, 36, 40, 48, 55, 56, 57, 59, 64, 68, 232, 320, 512, 514, 522, 536, 544, 831, 953, 968, 971, 972, 982, 988, 1018, 1145, 1147, 1313, 1315, 1316, 1341, 1361, 1362, 1367, 1374, 1397, 1398, 1399, 1400, 1407, 1415, 1452, 1495, 1517, 1525, 1582, 1589, 1661, 1680, 1727, 1823, 1835, 1836, 1880, 1982, 1997, 2014, 2094, 2097, 2098, 2099, 2100, 2102, 2110, 2112, 2119, 2120, 2128, 2131, 2133, 2136, 2148, 2149, 2156, 2158, 2170, 2171, 2176, 2179, 2190, 2191, 2193, 2198, 2201], "drive": [4, 10, 29, 2130], "facil": [4, 26, 1387, 2118], "analyz": [4, 5, 19, 55, 57, 68, 2135, 2136, 2139, 2147, 2149, 2204], "manipul": [4, 20, 59, 67, 1319, 2129, 2138, 2144, 2163, 2172, 2202], "1000000": [4, 1839], "mirror": [4, 138, 1772], "semant": [4, 10, 12, 18, 29, 36, 37, 50, 51, 55, 56, 59, 66, 68, 87, 149, 806, 903, 904, 905, 922, 943, 983, 1044, 1101, 1203, 1214, 1227, 1242, 1243, 1319, 1326, 1385, 1445, 1495, 1661, 1733, 1781, 1782, 1863, 2033, 2048, 2096, 2099, 2100, 2104, 2107, 2121, 2137, 2144, 2155, 2165, 2170, 2183, 2194], "number_per_run": 4, "raw_tim": 4, "task_spec": 4, "serializ": [4, 25, 2097], "consum": [4, 24, 31, 35, 39, 53, 56, 67, 434, 1145, 2118, 2133, 2139, 2146, 2151, 2155, 2175], "extrapol": 4, "sinc": [4, 8, 15, 24, 25, 29, 31, 33, 37, 38, 43, 51, 53, 55, 57, 59, 68, 314, 487, 533, 757, 795, 805, 822, 823, 833, 991, 1092, 1094, 1097, 1145, 1185, 1186, 1187, 1202, 1312, 1315, 1320, 1326, 1329, 1405, 1494, 1524, 1578, 1579, 1580, 1582, 1635, 1643, 1653, 1688, 1690, 1759, 1762, 1806, 1825, 1830, 1838, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1993, 2076, 2077, 2079, 2097, 2098, 2099, 2100, 2104, 2106, 2110, 2113, 2121, 2129, 2130, 2133, 2136, 2137, 2138, 2139, 2141, 2143, 2144, 2149, 2150, 2151, 2155, 2156, 2158, 2159, 2160, 2164, 2165, 2166, 2167, 2170, 2171, 2173, 2174, 2176, 2177, 2185, 2188, 2191, 2194, 2199, 2202, 2203, 2204], "properti": [4, 24, 29, 30, 31, 34, 35, 36, 38, 42, 47, 50, 55, 56, 59, 68, 804, 909, 935, 952, 962, 1041, 1070, 1161, 1162, 1164, 1166, 1167, 1168, 1176, 1177, 1178, 1315, 1490, 1582, 1616, 1641, 1746, 1762, 1773, 1774, 1775, 1776, 1816, 1880, 2065, 2100, 2104, 2113, 2126, 2130, 2133, 2136, 2141, 2152, 2157, 2159, 2165, 2170, 2172, 2173, 2176, 2179, 2184, 2190, 2191, 2192, 2193, 2205, 2209], "significant_figur": 4, "figur": [4, 8, 9, 31, 36, 68, 957, 2132, 2135, 2136, 2153, 2155, 2167, 2175, 2181, 2191, 2194, 2208], "intend": [4, 31, 50, 55, 57, 68, 919, 929, 932, 934, 1046, 1229, 1335, 1653, 1765, 1766, 1767, 1768, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 2100, 2128, 2130, 2144, 2155, 2158, 2203], "interquartil": 4, "mitig": [4, 70, 2139, 2147, 2172], "tail": [4, 24, 48], "645": 4, "conjunct": [4, 24, 29, 34, 59, 834, 842, 1653, 1688, 1772, 2163, 2165], "trim_sigfig": 4, "human": [4, 14, 35, 48, 55, 1088, 1102, 2128, 2155, 2192, 2205], "raw": [4, 68, 1199, 1374, 2104, 2133, 2158, 2159], "built_with_debug_symbol": 4, "baseline_inclusive_stat": 4, "baseline_exclusive_stat": 4, "stmt_inclusive_stat": 4, "stmt_exclusive_stat": 4, "stmt_callgrind_out": 4, "done": [4, 16, 24, 29, 31, 33, 34, 35, 36, 38, 48, 56, 59, 67, 68, 485, 495, 680, 770, 948, 955, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1331, 1405, 1496, 1497, 1498, 1518, 1521, 1533, 1544, 1545, 1546, 1556, 1557, 1564, 1565, 1566, 1567, 1592, 1622, 1628, 1630, 1680, 1772, 1865, 1872, 1880, 1988, 2100, 2110, 2112, 2118, 2130, 2132, 2133, 2135, 2136, 2137, 2138, 2144, 2147, 2152, 2153, 2155, 2157, 2160, 2161, 2165, 2167, 2173, 2189, 2190, 2191, 2193, 2200, 2202], "functioncount": 4, "stat": [4, 62, 812, 834, 850, 863, 864, 1101, 1109, 1110, 1111, 1622, 1772, 2083, 2084, 2113, 2133, 2180, 2194, 2204], "as_standard": 4, "strip": [4, 1690, 1772, 2097, 2156, 2170], "prefix": [4, 29, 31, 40, 50, 59, 68, 758, 767, 1315, 1582, 1772, 1880, 2095, 2106, 2130, 2155, 2158, 2160, 2180, 2201], "stumbl": 4, "filepath": 4, "dif": 4, "compon": [4, 8, 10, 16, 19, 20, 29, 36, 38, 68, 680, 923, 948, 1003, 1165, 1166, 1168, 1176, 1177, 1178, 1355, 1626, 1627, 1628, 1629, 1630, 1762, 1885, 1993, 2046, 2047, 2101, 2106, 2121, 2126, 2130, 2135, 2136, 2143, 2144, 2170, 2172, 2183, 2189, 2195, 2203, 2204], "locat": [4, 10, 15, 29, 31, 35, 38, 47, 55, 57, 153, 253, 485, 512, 680, 908, 983, 985, 1053, 1124, 1125, 1337, 1387, 1413, 1418, 1421, 1468, 1518, 1528, 1617, 1634, 1653, 1688, 1755, 1758, 1772, 1795, 1896, 1931, 1952, 1974, 1978, 2036, 2095, 2097, 2111, 2126, 2133, 2149, 2158, 2159, 2165, 2167, 2170, 2175, 2180, 2184, 2188, 2193, 2203], "someth": [4, 8, 55, 56, 61, 68, 937, 939, 1145, 1229, 1239, 1315, 1331, 1582, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 2008, 2102, 2113, 2118, 2119, 2121, 2130, 2142, 2150, 2155, 2165, 2189, 2190, 2191, 2194, 2196, 2202, 2203, 2204, 2205], "resembl": [4, 16, 2184], "23234231": 4, "first_build_dir": 4, "foo": [4, 15, 29, 47, 48, 55, 67, 68, 70, 78, 79, 834, 842, 883, 888, 1001, 1013, 1202, 1203, 1212, 1313, 1318, 1323, 1326, 1327, 1330, 1331, 1825, 2037, 2039, 2095, 2097, 2099, 2100, 2104, 2108, 2136, 2144, 2155, 2158, 2177, 2188, 2191, 2194, 2203, 2204], "9823794": 4, "bar": [4, 8, 47, 55, 68, 70, 78, 79, 883, 888, 1013, 1026, 1202, 1318, 1326, 1991, 1992, 2043, 2044, 2095, 2097, 2099, 2104, 2111, 2144, 2155, 2158, 2177], "53453": 4, "src": [4, 29, 68, 195, 227, 312, 320, 470, 511, 512, 513, 514, 515, 516, 518, 537, 1136, 1203, 1626, 1629, 1630, 1836, 1928, 1929, 1930, 1934, 1964, 2010, 2036, 2098, 2115, 2121, 2158, 2198], "function_that_actually_chang": 4, "second_build_dir": 4, "cancel": [4, 1312], "site": [4, 8, 2184], "denois": 4, "explan": [4, 10, 21, 26, 43, 891, 1315, 1582, 1880, 2135, 2136, 2144, 2194, 2200, 2204], "delta": [4, 38, 770, 1026, 1533, 1542, 1552, 1614, 1697, 1840, 1991, 1992, 2021, 2043, 2044, 2098, 2128], "inclus": [4, 38, 55, 86, 512, 1233, 1239, 1276, 1278, 1386, 1402, 1408, 1644, 1645, 1906, 1907, 1990, 2164, 2177], "diff": [4, 8, 2097, 2098, 2121, 2156], "One": [4, 9, 15, 29, 31, 55, 56, 62, 64, 68, 582, 980, 1003, 1203, 1214, 1306, 1635, 1726, 1773, 1781, 1782, 1789, 1793, 1863, 1868, 1877, 1906, 1907, 2031, 2048, 2097, 2099, 2100, 2120, 2131, 2132, 2133, 2136, 2143, 2149, 2159, 2167, 2175, 2185, 2189, 2190, 2192, 2193, 2201, 2208], "reason": [4, 9, 10, 24, 29, 31, 33, 40, 55, 59, 64, 680, 812, 870, 934, 935, 1166, 1168, 1315, 1331, 1332, 1351, 1352, 1379, 1416, 1494, 1521, 1582, 1690, 1740, 1795, 1880, 1927, 2097, 2099, 2100, 2104, 2106, 2121, 2130, 2135, 2137, 2139, 2147, 2149, 2155, 2165, 2173, 2189, 2190, 2191, 2193, 2194, 2197, 2203, 2204, 2205], "unit": [4, 12, 15, 38, 40, 51, 59, 68, 770, 771, 1163, 1181, 1490, 1500, 1523, 1526, 1531, 1532, 1533, 1534, 1601, 1602, 1612, 1653, 1677, 1681, 1686, 1687, 1734, 1743, 1799, 1800, 1804, 1807, 1809, 1812, 1908, 2023, 2078, 2114, 2130, 2132, 2139, 2141, 2158, 2187, 2188], "next": [4, 24, 29, 33, 35, 38, 50, 55, 56, 57, 59, 68, 559, 770, 1227, 1319, 1482, 1534, 1553, 1600, 1772, 1818, 2118, 2129, 2130, 2132, 2133, 2136, 2137, 2139, 2144, 2146, 2151, 2159, 2165, 2166, 2170, 2172, 2173, 2175, 2184, 2188, 2191, 2192, 2194], "logic": [4, 6, 13, 15, 24, 37, 41, 54, 57, 59, 68, 973, 975, 976, 978, 1008, 1009, 1010, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1203, 1229, 1319, 1397, 1398, 1399, 1400, 1410, 1588, 1772, 1894, 1921, 2007, 2100, 2101, 2133, 2135, 2136, 2137, 2141, 2160, 2191], "question": [4, 11, 24, 68, 1237, 1827, 2096, 2130, 2139, 2182, 2190, 2193], "involv": [4, 6, 8, 10, 12, 24, 29, 57, 59, 62, 64, 68, 1225, 1772, 1939, 2100, 2119, 2121, 2130, 2133, 2135, 2138, 2144, 2147, 2155, 2160, 2165, 2166, 2167, 2170, 2190, 2193, 2201, 2203], "look": [4, 5, 8, 9, 10, 13, 16, 29, 36, 38, 49, 50, 55, 56, 57, 61, 64, 68, 70, 80, 81, 485, 891, 937, 939, 1018, 1227, 1238, 1315, 1410, 1486, 1582, 1679, 1808, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1979, 1980, 1981, 1983, 1984, 2097, 2099, 2104, 2119, 2126, 2129, 2130, 2133, 2137, 2143, 2144, 2146, 2150, 2155, 2158, 2160, 2165, 2166, 2181, 2185, 2188, 2189, 2190, 2191, 2194, 2195, 2196, 2197, 2200, 2201, 2202, 2203, 2204, 2206], "autom": [4, 9, 68, 2097, 2160, 2184, 2194], "easili": [4, 8, 9, 12, 25, 29, 31, 35, 55, 1203, 1589, 1688, 1727, 1863, 1924, 2037, 2112, 2121, 2136, 2137, 2141, 2144, 2149, 2152, 2157, 2164, 2165, 2167, 2175, 2194, 2202, 2203, 2204], "exclus": [4, 24, 29, 38, 40, 50, 59, 68, 745, 1278, 1772, 1906, 1907, 1910, 2130, 2177], "basi": [4, 10, 11, 38, 680, 1388, 1868, 2133, 2143, 2160, 2165, 2183, 2188, 2203], "thought": [4, 47, 68, 1163, 1165, 1181, 1233, 2191], "path_and_function_nam": 4, "children": [4, 35, 43, 59, 68, 803, 1315, 1582, 1880, 2118, 2139, 2144, 2158, 2167], "identifi": [4, 8, 10, 29, 31, 40, 44, 47, 50, 51, 53, 68, 85, 485, 831, 1278, 1387, 1444, 1622, 2101, 2104, 2118, 2131, 2143, 2144, 2149, 2158, 2165, 2166, 2167, 2172, 2175, 2183, 2190, 2201, 2203], "hot": [4, 38, 1690, 1726, 2141, 2188], "spot": [4, 1781, 1782], "_data": 4, "truncate_row": 4, "_linewidth": 4, "subtract": [4, 312, 563, 894, 1199, 1690, 1979, 1980, 1981, 1983, 1984, 1994, 2098, 2156, 2170], "index": [4, 16, 20, 24, 25, 29, 31, 35, 38, 48, 55, 57, 68, 190, 207, 280, 311, 312, 313, 314, 315, 316, 318, 320, 321, 470, 471, 511, 512, 513, 514, 515, 516, 517, 518, 683, 684, 685, 689, 690, 691, 705, 706, 831, 883, 906, 921, 923, 934, 935, 966, 985, 1014, 1058, 1061, 1095, 1124, 1125, 1165, 1206, 1207, 1208, 1212, 1214, 1256, 1287, 1288, 1289, 1290, 1313, 1332, 1337, 1345, 1363, 1381, 1395, 1403, 1405, 1409, 1413, 1416, 1417, 1418, 1421, 1447, 1450, 1468, 1474, 1477, 1478, 1486, 1501, 1517, 1524, 1525, 1583, 1584, 1589, 1592, 1593, 1632, 1634, 1679, 1680, 1726, 1801, 1803, 1810, 1811, 1816, 1829, 1865, 1866, 1867, 1868, 1869, 1871, 1872, 1873, 1874, 1875, 1878, 1879, 1881, 1896, 1928, 1929, 1930, 1931, 1933, 1934, 1935, 1964, 1975, 1976, 1979, 1980, 1981, 1983, 1984, 1993, 2010, 2031, 2032, 2034, 2035, 2036, 2039, 2048, 2056, 2058, 2096, 2097, 2098, 2100, 2104, 2107, 2118, 2119, 2120, 2126, 2130, 2133, 2135, 2137, 2140, 2142, 2148, 2156, 2157, 2167, 2169, 2170, 2172, 2173, 2174, 2175, 2176, 2177, 2193, 2194, 2198, 2203, 2204], "cpython": [4, 15, 55, 68, 2182, 2192], "known": [4, 8, 10, 23, 28, 29, 34, 44, 50, 56, 60, 63, 65, 70, 80, 81, 1209, 1210, 1229, 1331, 1332, 1387, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1552, 1598, 1612, 1614, 1634, 1699, 1743, 1762, 1951, 1952, 1998, 2014, 2098, 2102, 2104, 2113, 2118, 2121, 2128, 2130, 2132, 2148, 2155, 2157, 2165, 2167, 2171, 2182, 2190, 2195, 2204], "quit": [4, 8, 68, 1781, 1782, 2100, 2126, 2136, 2138, 2158, 2165, 2201], "noisi": 4, "higher": [4, 8, 9, 25, 29, 33, 59, 60, 65, 70, 75, 79, 87, 149, 922, 924, 943, 1044, 1089, 1101, 1137, 1204, 1210, 1214, 1223, 1239, 1242, 1276, 1292, 1445, 1475, 1517, 1574, 1589, 1740, 1840, 1896, 1998, 2048, 2054, 2104, 2133, 2136, 2137, 2141, 2143, 2149, 2160, 2161, 2165, 2173, 2190, 2203], "filter": [4, 21, 543, 782, 783, 784, 1312, 1335, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1993, 2100, 2158, 2204], "transform": [4, 13, 24, 31, 35, 36, 37, 55, 59, 64, 66, 414, 791, 810, 865, 867, 886, 887, 992, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1202, 1203, 1204, 1208, 1213, 1236, 1312, 1325, 1499, 1536, 1544, 1545, 1546, 1554, 1569, 1588, 1611, 1627, 1628, 1629, 1630, 1653, 1659, 1688, 1705, 1725, 1762, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1855, 1859, 1860, 1861, 1862, 1863, 1959, 1993, 2108, 2121, 2137, 2139, 2140, 2160, 2170, 2175, 2181, 2182, 2184, 2192, 2196, 2203], "rather": [4, 9, 10, 15, 29, 40, 43, 53, 55, 57, 59, 68, 70, 79, 789, 795, 961, 991, 1014, 1313, 1316, 1387, 1478, 1653, 1688, 1699, 1759, 1790, 1939, 1943, 2097, 2100, 2120, 2121, 2131, 2133, 2136, 2137, 2155, 2158, 2160, 2165, 2170, 2175, 2188, 2189, 2191, 2193, 2194, 2195, 2196, 2203], "unicod": [4, 2101], "dictionari": [4, 15, 24, 25, 31, 35, 36, 38, 55, 63, 68, 414, 680, 804, 805, 806, 810, 829, 830, 831, 865, 867, 868, 883, 887, 893, 968, 1001, 1101, 1202, 1212, 1222, 1229, 1315, 1316, 1323, 1327, 1331, 1332, 1387, 1388, 1458, 1459, 1464, 1524, 1525, 1582, 1583, 1592, 1679, 1806, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 2063, 2081, 2082, 2099, 2101, 2136, 2144, 2155, 2157, 2165, 2175, 2180, 2199, 2205, 2206], "lookup": [4, 31, 38, 50, 1524, 1679, 2097, 2101, 2132, 2166, 2197], "map": [4, 15, 29, 31, 36, 38, 40, 47, 48, 50, 51, 55, 56, 57, 59, 64, 65, 68, 70, 79, 232, 694, 745, 804, 805, 809, 810, 813, 828, 829, 831, 837, 867, 868, 883, 884, 885, 886, 887, 893, 914, 982, 1044, 1197, 1203, 1214, 1225, 1227, 1229, 1234, 1269, 1278, 1323, 1326, 1387, 1388, 1512, 1513, 1514, 1520, 1521, 1522, 1526, 1528, 1578, 1579, 1580, 1583, 1592, 1674, 1675, 1676, 1681, 1789, 1806, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1896, 1898, 2032, 2033, 2048, 2054, 2101, 2104, 2112, 2120, 2122, 2130, 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2167, 2191, 2193, 2203], "account": [5, 35, 48, 68, 1495, 1772, 2126, 2128, 2132, 2139, 2170, 2188], "heavili": [5, 1846, 2132, 2136, 2158], "similarli": [5, 8, 29, 31, 35, 55, 67, 68, 770, 802, 806, 887, 992, 1165, 1315, 1371, 1582, 1630, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 1880, 2024, 2025, 2026, 2027, 2099, 2104, 2130, 2136, 2138, 2141, 2147, 2170, 2193, 2196, 2201], "platform": [5, 9, 10, 15, 29, 42, 43, 47, 1362, 1363, 1374, 1895, 1997, 2110, 2142, 2147, 2148, 2160, 2184], "startup": [5, 20], "slower": [5, 15, 29, 35, 905, 938, 1185, 1186, 1187, 1345, 1352, 1707, 1789, 1840, 1841, 2136, 2148, 2157, 2171, 2193, 2203], "rerun": [6, 29, 2133], "segment": [6, 1101, 1199, 1218, 1614, 2133, 2158, 2194, 2199, 2203, 2206], "persist": [6, 22, 31, 55, 59, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 770, 829, 830, 831, 1315, 1533, 1552, 1582, 1598, 1773, 1775, 1880, 2144, 2149, 2150, 2172, 2183, 2188, 2193, 2197], "rng": [6, 24, 1073, 1116, 1429, 1440, 1454, 1462, 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43, 2097, 2130, 2137, 2141, 2191, 2195], "maker": 10, "strong": 10, "toward": [10, 70, 1140, 1277, 1278, 1388, 1482, 1772, 1872, 1915, 1923, 1924, 2139], "philosophi": [10, 63, 2096], "beyond": [10, 25, 57, 1439, 1517, 1843, 1844, 1859, 1922, 2115, 2138, 2144, 2190], "encourag": [10, 15, 40, 2160, 2170, 2177], "propos": [10, 1839, 1866, 1867, 1880, 2107, 2141, 2157, 2166], "review": [10, 11, 25, 2110, 2158, 2196], "willing": [10, 2183], "invest": 10, "anyon": 10, "ownership": [10, 68], "codebas": [10, 2136, 2203], "strictli": [10, 24, 149, 190, 207, 485, 922, 985, 1278, 1315, 1319, 1582, 1880, 2130, 2171], "compani": 10, "bui": 10, "addition": [10, 15, 24, 25, 29, 31, 36, 38, 40, 55, 57, 59, 149, 512, 616, 922, 957, 1203, 1207, 1208, 1319, 1379, 1486, 1544, 1545, 1546, 1973, 2138, 2179, 2203, 2206], "membership": [10, 40, 49, 50, 2101], "That": [10, 13, 18, 20, 40, 48, 51, 61, 68, 1014, 1018, 1312, 1386, 1402, 2036, 2104, 2110, 2136, 2137, 2138, 2139, 2149, 2158, 2165, 2191, 2194], "seat": 10, "reserv": [10, 33, 47, 1101, 2081, 2101, 2133, 2144, 2206], "emploi": [10, 31, 1862, 2144, 2158, 2184], "directori": [10, 15, 29, 31, 35, 40, 48, 1837, 2095, 2111, 2143, 2155, 2158, 2159, 2175, 2184, 2185, 2194, 2204], "procedur": [10, 38, 888, 1331, 1332, 1388, 1971, 2165, 2204], "disput": 10, "made": [10, 18, 21, 29, 31, 34, 51, 55, 68, 929, 932, 934, 949, 1042, 1315, 1582, 1628, 1630, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1851, 1853, 1859, 1860, 1861, 1862, 1863, 1880, 1912, 2099, 2107, 2130, 2150, 2158, 2175, 2177, 2188, 2190, 2191, 2203, 2204, 2205], "public": [10, 11, 36, 1203, 2121, 2136, 2203, 2205], "resolut": [10, 1594, 1595, 1653, 1688, 1730, 1731, 2021, 2101, 2158, 2209], "conclus": 10, "publicli": [10, 2205], "vision": [10, 1325, 1517, 1671, 2095, 2196], "roadmap": [10, 11], "parti": [10, 50, 2095, 2097, 2104, 2133, 2137, 2144, 2158, 2191], "triag": [10, 11], "meet": [10, 11, 13, 31, 49, 1013, 1018, 1345, 2133, 2194], "Their": [10, 1137, 2136, 2193], "articul": 10, "cohes": 10, "negoti": [10, 2165], "contenti": 10, "broad": [10, 2144, 2207], "stakehold": 10, "power": [10, 13, 33, 49, 1108, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1188, 1339, 1369, 1383, 1549, 1550, 1551, 1709, 1710, 1711, 1790, 1824, 1839, 1876, 1892, 1916, 2042, 2101, 2133, 2170], "veto": 10, "admin": 10, "amongst": 10, "commonli": [10, 38, 59, 1417, 1841, 2100, 2102, 2121, 2129, 2130, 2139, 2157, 2160, 2173, 2182, 2187, 2190], "merit": 10, "demonstr": [10, 35, 55, 68, 70, 78, 79, 1501, 2097, 2144, 2149, 2152, 2153, 2157, 2165, 2172, 2184, 2192, 2194, 2196, 2201], "expertis": 10, "align": [10, 25, 55, 770, 789, 795, 838, 850, 1001, 1145, 1269, 1371, 1493, 1501, 1517, 1533, 1547, 1552, 1576, 1577, 1598, 1635, 1643, 1644, 1645, 1671, 1672, 1699, 1700, 1740, 1759, 1789, 1830, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1866, 2021, 2119, 2126, 2130, 2141, 2149, 2155, 2163, 2171], "continu": [10, 24, 29, 31, 38, 54, 57, 59, 68, 401, 607, 836, 938, 1269, 1351, 1352, 1379, 1500, 1501, 1772, 1877, 2101, 2107, 2130, 2139, 2149, 2159, 2172, 2179, 2189, 2191, 2192, 2194, 2203], "light": [10, 2175, 2203], "mainten": [10, 50, 51], "emeritu": [10, 11], "inact": [10, 1101, 2133, 2206], "contact": [10, 29], "item": [10, 24, 31, 55, 70, 80, 81, 582, 583, 584, 588, 804, 805, 806, 828, 829, 830, 831, 968, 1014, 1517, 1583, 1592, 1795, 1943, 2095, 2097, 2098, 2099, 2100, 2108, 2119, 2121, 2133, 2140, 2149, 2155, 2156, 2157, 2158, 2165, 2175, 2176, 2188, 2190, 2191, 2193, 2198, 2203], "nomine": 10, "breadth": [10, 35], "testimoni": 10, "posit": [10, 24, 31, 35, 36, 38, 55, 68, 86, 401, 470, 544, 745, 886, 890, 892, 970, 972, 979, 992, 993, 994, 1044, 1161, 1162, 1163, 1164, 1165, 1173, 1175, 1179, 1181, 1199, 1209, 1210, 1213, 1227, 1229, 1273, 1274, 1307, 1310, 1315, 1318, 1345, 1346, 1357, 1359, 1370, 1373, 1374, 1388, 1408, 1423, 1472, 1477, 1478, 1495, 1509, 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2098, 2119, 2121, 2128, 2130, 2136, 2137, 2155, 2156, 2164, 2170, 2171, 2190, 2194, 2198], "interact": [10, 16, 24, 29, 36, 68, 891, 944, 1040, 1075, 1079, 2069, 2101, 2104, 2130, 2136, 2158, 2160, 2175, 2188, 2191, 2194, 2197, 2206], "final": [10, 20, 29, 31, 34, 38, 40, 50, 55, 56, 57, 59, 63, 64, 696, 699, 700, 770, 969, 987, 1000, 1145, 1184, 1203, 1242, 1269, 1387, 1405, 1410, 1502, 1533, 1535, 1552, 1598, 1611, 1762, 1841, 1974, 2021, 2097, 2099, 2100, 2101, 2119, 2136, 2139, 2141, 2144, 2147, 2149, 2151, 2152, 2155, 2157, 2158, 2166, 2167, 2188, 2190, 2192, 2203, 2204], "declin": 10, "conflict": [10, 25, 31, 51, 2158], "lack": [10, 12, 35, 968, 1351, 1352, 1379], "unfit": 10, "conduct": [10, 1772, 1885, 1998, 2165, 2184, 2188], "filial": 10, "romant": 10, "strength": 10, "candid": [10, 961, 2158], "letter": [10, 1145], "befit": 10, "candidaci": 10, "behind": [10, 29, 55, 2096, 2149, 2166, 2193], "75": [10, 1277, 1570, 1688, 1706, 1839, 1896, 2098, 2171], "unforeseen": 10, "circumst": [10, 50, 1001, 1239, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 2133], "perman": [10, 68, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 2130, 2167], "unavail": [10, 1056, 2099], "rank": [10, 24, 25, 29, 30, 31, 34, 35, 36, 37, 38, 40, 44, 48, 50, 51, 54, 56, 59, 70, 79, 1361, 1362, 1363, 1370, 1405, 1574, 1622, 1772, 1795, 1885, 1998, 2098, 2135, 2139, 2146, 2155, 2165, 2166, 2183, 2190, 2203, 2204, 2208], "elect": 10, "invit": [10, 2095], "convinc": 10, "approach": [10, 25, 29, 33, 68, 989, 1214, 1495, 1880, 1939, 1971, 2048, 2097, 2118, 2133, 2136, 2141, 2151, 2152, 2153, 2157, 2160, 2165, 2194, 2203], "interview": 10, "talk": [10, 53, 2104, 2143], "gather": [10, 29, 31, 33, 35, 36, 50, 59, 512, 2011, 2036, 2098, 2136, 2138, 2139, 2143, 2156, 2158, 2198], "read": [10, 13, 19, 20, 24, 29, 31, 36, 39, 40, 43, 50, 56, 59, 63, 68, 447, 955, 1018, 1103, 1128, 1196, 1198, 1199, 1231, 1323, 1387, 1877, 2104, 2119, 2120, 2126, 2130, 2131, 2133, 2135, 2137, 2143, 2147, 2151, 2158, 2160, 2165, 2188, 2191, 2192, 2194, 2196, 2203], "attend": [10, 745, 1588, 1626], "confer": [10, 1535], "pipelin": [10, 31, 68, 2096, 2165, 2194], "world": [10, 29, 33, 34, 40, 48, 50, 51, 55, 59, 1622, 1772, 2130, 2158, 2160, 2190, 2191, 2192, 2203], "cover": [10, 56, 57, 60, 68, 70, 1119, 1145, 1575, 1713, 1714, 1715, 2100, 2102, 2120, 2130, 2136, 2139, 2141, 2143, 2144, 2165, 2192, 2197, 2201, 2207], "push": [10, 42, 1105, 1107, 1214, 1381, 2048, 2149, 2159, 2191, 2203], "codeown": 10, "notifi": [10, 30, 51, 2167], "expert": [10, 33, 2157], "strongli": [10, 29, 40, 51, 57, 1520, 1521, 1522, 1526, 1993, 2095], "failur": [10, 29, 38, 40, 42, 43, 47, 48, 49, 50, 54, 55, 680, 948, 949, 1001, 1003, 1203, 1229, 1331, 1332, 1439, 2100, 2106, 2115, 2118, 2139, 2165, 2167, 2177, 2191, 2194, 2203], "revert": [10, 38, 59, 1619, 1748, 2167], "substanti": [10, 25, 2133, 2183, 2188, 2203], "syntact": [10, 43, 68], "incompat": [10, 15, 64, 982, 1312, 1781, 1782, 2131, 2158, 2188, 2203], "establish": [10, 20, 29, 50, 1877, 2121, 2130], "seri": [10, 35, 68, 1501, 1544, 2140, 2148, 2155, 2188, 2199, 2205], "lf": 10, "llc": 10, "guidelin": [10, 1806, 2146, 2151, 2158, 2160, 2161], "trademark": 10, "www": [10, 1501, 2175], "lfproject": 10, "acknowledg": [10, 29, 2167, 2170], "copyright": [10, 2185, 2204], "holder": 10, "independ": [10, 24, 29, 50, 55, 59, 67, 152, 153, 789, 795, 937, 938, 939, 940, 941, 942, 953, 1044, 1269, 1278, 1374, 1519, 1520, 1521, 1522, 1526, 1674, 1675, 1676, 1681, 1698, 1699, 1759, 1791, 1827, 1895, 2054, 2097, 2129, 2130, 2133, 2149, 2158, 2172, 2201, 2203], "authorship": 10, "claus": [10, 2138], "bsd": 10, "licens": 10, "opensourc": 10, "outbound": 10, "inbound": 10, "q": [10, 25, 38, 430, 478, 768, 813, 1259, 1336, 1352, 1355, 1374, 1416, 1475, 1547, 1588, 1642, 1789, 1883, 1885, 1895, 1896, 1998, 2097, 2098, 2126, 2141, 2148, 2155, 2170, 2171, 2193], "partli": [10, 2100], "domain": [10, 38, 43, 694, 850, 853, 914, 1159, 1160, 1161, 1162, 1164, 1165, 1166, 1167, 1168, 1173, 1175, 1176, 1177, 1178, 1269, 2121, 2155, 2160], "absolut": [10, 12, 15, 68, 91, 681, 704, 948, 949, 1193, 1304, 1344, 1349, 1370, 1373, 1375, 1396, 1542, 1548, 1614, 1697, 1701, 1744, 1800, 1801, 1803, 1804, 1806, 1809, 1810, 1811, 1812, 1830, 1838, 1889, 1915, 1946, 2098, 2108, 2144, 2155, 2156, 2171, 2176, 2177, 2184], "health": 10, "success": [10, 31, 38, 40, 47, 68, 285, 1346, 1357, 1359, 1405, 1802, 1979, 1980, 1981, 1983, 1984, 2133, 2144, 2155, 2170, 2204], "am": 10, "grant": 10, "purchas": 10, "board": 10, "driven": [10, 2155], "clearli": [10, 1229, 2107, 2158], "sponsorship": 10, "foundat": 10, "ptf": 10, "minor": [10, 1068, 1346, 1362, 1453, 2170, 2191], "committ": 10, "prior": [10, 20, 25, 29, 31, 40, 929, 933, 934, 935, 1006, 1388, 1628, 1630, 1740, 1806, 2126, 2131, 2133, 2136, 2157, 2160, 2188, 2194], "walkthrough": [10, 2129], "facebook": 10, "infrastructur": [10, 35, 42, 2158, 2190], "employe": 10, "expand": [10, 38, 254, 492, 907, 929, 933, 934, 935, 948, 949, 984, 1368, 1369, 1417, 1588, 1633, 1733, 2031, 2095, 2098, 2106, 2119, 2120, 2131, 2133, 2136, 2137, 2155, 2156, 2158, 2174, 2198], "deliv": [10, 2159], "offici": [10, 29, 1486, 2140, 2160], "showcas": [10, 13, 37, 1578, 2133, 2146], "whenev": [10, 36, 55, 57, 909, 1789, 1790, 2113, 2118, 2121, 2136, 2163, 2166, 2167, 2193, 2203, 2204, 2205], "fix": [11, 19, 24, 29, 38, 40, 50, 55, 57, 61, 64, 68, 261, 807, 815, 957, 1203, 1329, 1361, 1524, 1525, 1601, 1679, 1680, 1699, 1772, 1881, 2097, 2098, 2113, 2128, 2129, 2133, 2138, 2139, 2146, 2150, 2152, 2155, 2156, 2157, 2188, 2191, 2194, 2201, 2203, 2204], "plu": [11, 15, 31, 937, 1199, 1362, 2139, 2170], "quarterli": 11, "chintala": 11, "edward": 11, "yang": [11, 1388], "ezyang": [11, 2104, 2174, 2203], "greg": 11, "chanan": 11, "gchanan": 11, "dmytro": 11, "dzhulgakov": 11, "nikita": 11, "shulga": 11, "malfet": 11, "alban": 11, "desmaison": 11, "alband": 11, "piotr": 11, "bialecki": 11, "ptrblck": 11, "mikayla": 11, "gawarecki": 11, "mikaylagawarecki": 11, "joel": [11, 1885, 1998], "schlosser": 11, "jbschlosser": 11, "sam": 11, "gross": 11, "colesburi": 11, "adam": [11, 30, 31, 34, 38, 59, 957, 1844, 1845, 1847, 1859, 1863, 2157, 2203], "paszk": 11, "apaszk": 11, "jane": 11, "xu": [11, 59], "janeyx99": 11, "ilqar": 11, "ramazanli": 11, "iramazanli": 11, "vincent": 11, "quennevil": 11, "belair": 11, "vincentqb": 11, "jeffrei": 11, "wan": 11, "soulitz": 11, "animesh": 11, "jain": 11, "anijain2305": 11, "jason": [11, 2192], "ansel": [11, 2192], "jansel": 11, "elia": 11, "ellison": 11, "eellison": 11, "horac": 11, "he": [11, 2128], "chille": 11, "shunt": 11, "zhang": 11, "shunting314": [11, 2197], "jiong": 11, "gong": 11, "jgong5": 11, "brian": 11, "hirsh": 11, "bdhirsh": [11, 2039], "richard": [11, 38], "zou": 11, "zou3519": 11, "avik": 11, "chaudhuri": 11, "avikchaudhuri": 11, "yanan": 11, "cao": 11, "gmagogsfm": 11, "bin": [11, 40, 48, 50, 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1003, 1214, 1227, 2048, 2120, 2121, 2130, 2135, 2189, 2190, 2195, 2196], "real_param": 12, "p": [12, 25, 31, 38, 57, 59, 68, 153, 233, 285, 424, 425, 452, 487, 490, 491, 696, 699, 969, 971, 981, 989, 1139, 1336, 1347, 1353, 1354, 1362, 1363, 1405, 1407, 1410, 1420, 1471, 1490, 1519, 1520, 1521, 1522, 1524, 1525, 1526, 1547, 1549, 1550, 1551, 1587, 1591, 1593, 1631, 1632, 1654, 1673, 1674, 1675, 1676, 1679, 1680, 1681, 1709, 1710, 1711, 1721, 1725, 1728, 1729, 1740, 1756, 1778, 1787, 1793, 1795, 1801, 1810, 1830, 1862, 1916, 1973, 2098, 2099, 2129, 2130, 2133, 2138, 2141, 2146, 2157, 2159, 2170, 2171, 2197, 2198], "complex_optim": 12, "adamw": [12, 1843, 1847, 1859, 2157], "real_optim": 12, "slight": [12, 1935, 2170, 2173], "discrep": [12, 1772, 1902, 2141], "foreach": [12, 1777, 1778, 1779, 1780, 1787, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 2157], "v": [12, 31, 35, 47, 55, 63, 68, 486, 487, 543, 927, 938, 940, 941, 942, 968, 985, 1145, 1202, 1212, 1214, 1229, 1312, 1329, 1351, 1379, 1383, 1388, 1588, 1631, 1632, 1642, 1679, 1725, 1791, 1822, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1885, 1918, 1978, 1982, 1997, 1998, 2048, 2097, 2098, 2099, 2100, 2113, 2130, 2136, 2141, 2150, 2157, 2158, 2170, 2175, 2187], "forloop": 12, "numerical_accuraci": 12, "impact": [12, 25, 29, 57, 1013, 1101, 1939, 2110, 2113, 2121, 2130, 2160, 2174, 2194, 2199, 2203, 2204], "pointwis": [12, 38, 1001, 1207, 1208, 1393, 1394, 1547, 2119, 2121, 2131, 2193, 2196, 2197], "lbfg": [12, 2157], "yet": [12, 29, 30, 35, 36, 40, 55, 56, 59, 64, 67, 68, 77, 485, 852, 1039, 1119, 1240, 1318, 1320, 1333, 1421, 1772, 1797, 1801, 1803, 1843, 1844, 2096, 2099, 2100, 2106, 2112, 2120, 2121, 2133, 2149, 2155, 2156, 2160, 2165, 2167, 2170, 2171, 2173, 2188, 2191, 2193, 2194, 2199, 2201, 2203, 2204], "fulli": [12, 15, 24, 29, 31, 33, 34, 35, 37, 55, 63, 68, 680, 1108, 1227, 1315, 1578, 1579, 1580, 1582, 1880, 2100, 2106, 2112, 2136, 2158, 2160, 2190, 2191, 2198, 2203], "quantiz": [12, 28, 55, 68, 218, 325, 337, 471, 472, 473, 474, 475, 477, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 887, 888, 889, 890, 891, 892, 893, 961, 1130, 1159, 1160, 1897, 1898, 1899, 1900, 1901, 2036, 2096, 2156, 2173, 2176, 2177, 2180, 2181], "pred": [13, 57, 70, 73, 78, 79, 1018, 1547, 1772, 2145, 2157, 2165, 2198, 2203], "union": [13, 15, 24, 31, 33, 35, 36, 37, 40, 44, 48, 51, 55, 56, 59, 68, 1001, 1018, 1221, 1224, 1225, 1227, 1229, 1255, 1287, 1301, 1327, 1387, 1483, 1484, 1485, 1487, 1488, 1489, 1491, 1492, 1493, 1529, 1530, 1549, 1550, 1551, 1575, 1576, 1577, 1582, 1626, 1628, 1630, 1633, 1646, 1685, 1795, 1816, 1895, 1927, 1988, 2039, 2098, 2099, 2100, 2104, 2121, 2126, 2149, 2155, 2158, 2172, 2175, 2177, 2181], "true_fn": [13, 56, 57, 70, 73, 78, 79, 1018], "false_fn": [13, 56, 57, 70, 73, 78, 79, 1018], "operand": [13, 57, 70, 78, 79, 977, 1018, 1145, 2100, 2101, 2136, 2170, 2173], "Its": [13, 29, 56, 68, 1026, 1053, 1315, 1363, 1381, 1382, 1407, 1582, 1830, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1977, 2045, 2100, 2135, 2172], "uniqu": [13, 29, 40, 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[2, 2100, 2130, 2179, 2200, 2202], "disabl": [2, 24, 1007, 2097, 2130, 2179, 2194, 2195, 2203], "comput": [2, 65, 2130, 2147, 2166, 2179, 2194], "default": [2, 24, 25, 2099, 2130, 2133, 2141, 2162, 2191], "layout": [2, 2121, 2173], "manual": [2, 35], "In": [2, 9, 2130, 2131, 2179], "place": [2, 64, 2119, 2130, 2131, 2155, 2179], "oper": [2, 11, 20, 25, 29, 55, 57, 64, 84, 2099, 2100, 2105, 2107, 2119, 2120, 2121, 2130, 2134, 2136, 2143, 2150, 2155, 2156, 2160, 2170, 2176, 2179, 2193, 2201, 2203], "tensor": [2, 12, 16, 36, 37, 55, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 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8, "discuss": 8, "submit": 8, "pull": 8, "request": 8, "open": 8, "review": 8, "code": [8, 68, 2097, 2133, 2140, 2158, 2185, 2194, 2203], "readabl": 8, "test": [8, 2100, 2104, 2136, 2158, 2177, 2200], "case": [8, 2097, 2172], "make": [8, 10, 2188, 2191], "codebas": 8, "more": [8, 55, 60, 2165, 2182], "robust": 8, "triag": 8, "about": [8, 2130, 2165, 2193], "sourc": [8, 2140, 2148, 2150, 2158], "develop": [8, 55, 2096, 2151, 2182], "common": [8, 29, 68, 2143, 2160], "mistak": 8, "To": 8, "avoid": [8, 2146, 2148, 2155, 2158], "frequent": [8, 2097, 2138, 2155, 2160, 2194], "ask": [8, 2097, 2138, 2155, 2160, 2194], "question": [8, 2097, 2138, 2155, 2160, 2194], "On": [8, 25, 2099], "python": [8, 9, 29, 55, 64, 71, 72, 73, 74, 75, 76, 77, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2104, 2136, 2148, 2155, 2184, 2191], "doc": [8, 11, 2121, 2142], "c": [8, 11, 16, 2104, 2130, 2142, 2149, 2155, 2184], "overview": [8, 19, 20, 55, 68, 2151, 2152, 2158, 2159, 2170, 2189, 2192], "design": [9, 2100, 2135, 2165, 2166, 2167], "philosophi": 9, "principl": [9, 10], "1": [9, 35, 51, 62, 2097, 2137, 2191], "usabl": 9, "perform": [9, 11, 2144, 2149, 2193, 2200, 2201, 2204], "2": [9, 35, 62, 2097, 2133, 2137, 2138, 2160, 2163, 2199, 2200, 2204], "simpl": [9, 2099, 2100, 2144, 2152, 2166, 2203], "easi": 9, "3": [9, 51, 62], "first": [9, 2158], "best": [9, 2133, 2146, 2160, 2187], "class": [9, 36, 43, 68, 2097, 2099, 2100, 2102, 2155, 2157, 2158, 2176], "languag": [9, 2096, 2097, 2099, 2100, 2101], "interoper": 9, "mechan": [10, 2121, 2127, 2130, 2141], "summari": [10, 2160, 2203], "modul": [10, 11, 57, 61, 63, 68, 1582, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 2097, 2098, 2099, 2100, 2102, 2122, 2130, 2136, 2144, 2149, 2158, 2160, 2199], "core": [10, 11, 2198], "lead": [10, 11], "bdfl": [10, 11], "nomin": [10, 2100], "confirm": 10, "remov": [10, 1813, 2119], "The": [10, 68, 2100, 2130, 2190], "re": [10, 2158], "scope": 10, "project": 10, "decis": 10, "uncontroversi": 10, "chang": 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1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1793, 1794, 1795, 1796, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 2099, 2100, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2130, 2133, 2136, 2149, 2163, 2170, 2199], "optim": [11, 12, 34, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 2129, 2130, 2133, 2157, 2165, 2166, 2179], "torchdynamo": [11, 2135, 2151, 2152, 2153, 2154, 2188, 2195, 2203, 2204], "torchinductor": [11, 2197, 2200, 2204], "cudagraph": [11, 1037, 2188], "tree": [11, 2188], "pt2": [11, 2163], "dispatch": 11, "export": [11, 55, 56, 57, 2151, 2152, 2153, 2155, 2158, 2160, 2163, 2179, 2184, 2194], "aot": 11, "inductor": 11, "aoti": 11, "runtim": [11, 2132, 2138, 2204], "compil": [11, 63, 1001, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 2121, 2133, 2140, 2182, 2183, 2184, 2186, 2194, 2195, 2201, 2203, 2204], "jit": [11, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1331, 1332, 1333, 1334, 2097, 2100, 2103], "torchscript": [11, 16, 2097, 2098, 2099, 2100, 2102, 2132, 2143, 2151, 2153, 2155, 2156, 2158], "deploi": [11, 27], "distribut": [11, 29, 31, 32, 33, 34, 35, 36, 37, 38, 2122, 2127, 2135, 2142, 2144, 2165, 2166, 2194], "rng": 11, "multiprocess": [11, 48, 2118, 2133, 2146, 2150], "linear": [11, 12, 742, 743, 766, 774, 791, 1210, 1569, 1705, 2122, 2127, 2147, 2170], "algebra": [11, 12, 2147, 2170], "linalg": [11, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 2105, 2147], "spars": [11, 1969, 1970, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 2122, 2127, 2170], "nestedtensor": 11, "nest": [11, 2121], "maskedtensor": [11, 2107], "mask": [11, 2107], "fast": [11, 58, 2141, 2166], "fourier": [11, 58], "transform": [11, 38, 58, 60, 61, 63, 65, 68, 1626, 2122, 2136, 2144, 2194, 2202], "fft": [11, 58, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182], "amd": [11, 2147], "rocm": [11, 20, 2142], "hip": [11, 2142], "tool": [11, 18, 36, 2161, 2170, 2184, 2203], "c10": 11, "onnx": [11, 2151, 2152, 2153, 2154, 2155, 2156], "liteinterpret": 11, "quantiz": [11, 886, 2122, 2144, 2155, 2160, 2161, 2162, 2163], "ao": [11, 2163, 2180, 2181], "window": [11, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2150, 2168], "appl": 11, "m1": 11, "metal": 11, "powerpc": 11, "x86": [11, 2187], "aarch64": 11, "librari": [11, 2096, 2104, 2148], "xla": 11, "torchserv": 11, "torchvis": [11, 62], "torchtext": 11, "torchaudio": 11, "torchrec": 11, "torchx": 11, "torchdata": 11, "torcharrow": 11, "executorch": 11, "edg": [11, 2097, 2158], "mobil": 11, "torchtun": 11, 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"transformer"]], "One-to-One Pass": [[2202, "one-to-one-pass"]], "One-to-X Pass": [[2202, "one-to-x-pass"]], "One-to-None Pass": [[2202, "one-to-none-pass"]], "Utilizing Local Information": [[2202, "utilizing-local-information"]], "Subgraph Rewriter": [[2202, "subgraph-rewriter"]], "Pass Manager": [[2202, "pass-manager"]], "Partitioner": [[2202, "partitioner"]], "Subgraph Matcher": [[2202, "subgraph-matcher"]], "Capability Based Partitioner": [[2202, "capability-based-partitioner"]], "Frequently Asked Questions": [[2194, "frequently-asked-questions"], [2097, "frequently-asked-questions"], [2160, "frequently-asked-questions"], [2155, "frequently-asked-questions"], [2138, "frequently-asked-questions"], [8, "frequently-asked-questions"]], "Does torch.compile support training?": [[2194, "does-torch-compile-support-training"]], "Do you support Distributed code?": [[2194, "do-you-support-distributed-code"]], "Do I still need to export whole graphs?": [[2194, "do-i-still-need-to-export-whole-graphs"]], "Why is my code crashing?": [[2194, "why-is-my-code-crashing"]], "Why is compilation slow?": [[2194, "why-is-compilation-slow"]], "Why are you recompiling in production?": [[2194, "why-are-you-recompiling-in-production"]], "How are you speeding up my code?": [[2194, "how-are-you-speeding-up-my-code"]], "Why am I not seeing speedups?": [[2194, "why-am-i-not-seeing-speedups"]], "Graph Breaks": [[2194, "graph-breaks"], [2204, "graph-breaks"], [55, "graph-breaks"]], "Identifying the cause of a graph break": [[2194, "identifying-the-cause-of-a-graph-break"]], "Why didn\u2019t my code recompile when I changed it?": [[2194, "why-didnt-my-code-recompile-when-i-changed-it"]], "Why am I getting incorrect results?": [[2194, "why-am-i-getting-incorrect-results"]], "Why am I getting OOMs?": [[2194, "why-am-i-getting-ooms"]], "Does torch.func work with torch.compile (for grad and vmap transforms)?": [[2194, "does-torch-func-work-with-torch-compile-for-grad-and-vmap-transforms"]], "Calling torch.func transform inside of a function handled with torch.compile": [[2194, "calling-torch-func-transform-inside-of-a-function-handled-with-torch-compile"]], "Compiling torch.func.grad with torch.compile": [[2194, "compiling-torch-func-grad-with-torch-compile"]], "Compiling torch.vmap with torch.compile": [[2194, "compiling-torch-vmap-with-torch-compile"]], "Compiling functions besides the ones which are supported (escape hatch)": [[2194, "compiling-functions-besides-the-ones-which-are-supported-escape-hatch"]], "Does NumPy work with torch.compile?": [[2194, "does-numpy-work-with-torch-compile"]], "Which NumPy features does torch.compile support?": [[2194, "which-numpy-features-does-torch-compile-support"]], "Can I compile NumPy code using torch.compile?": [[2194, "can-i-compile-numpy-code-using-torch-compile"]], "Can I execute NumPy code on CUDA and compute gradients via torch.compile?": [[2194, "can-i-execute-numpy-code-on-cuda-and-compute-gradients-via-torch-compile"]], "How do I debug NumPy code under torch.compile?": [[2194, "how-do-i-debug-numpy-code-under-torch-compile"]], "I torch.compile some NumPy code and I did not see any speed-up.": [[2194, "i-torch-compile-some-numpy-code-and-i-did-not-see-any-speed-up"]], "Which API to use for fine grain tracing?": [[2194, "which-api-to-use-for-fine-grain-tracing"]], "How do I graph break on a function?": [[2194, "how-do-i-graph-break-on-a-function"]], "What\u2019s the difference between torch._dynamo.disable and torch._dynamo.disallow_in_graph": [[2194, "what-s-the-difference-between-torch-dynamo-disable-and-torch-dynamo-disallow-in-graph"]], "What\u2019s the difference between torch._dynamo.disable and torch._dynamo_skip": [[2194, "what-s-the-difference-between-torch-dynamo-disable-and-torch-dynamo-skip"]], "TorchInductor GPU Profiling": [[2197, "torchinductor-gpu-profiling"]], "Relevant Environment Variables": [[2197, 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"reporting-issues"]], "Ablation": [[2203, "ablation"]], "Bisecting": [[2203, "bisecting"]], "Creating a reproducer": [[2203, "creating-a-reproducer"]], "Minifier": [[2203, "minifier"]], "Debugging Deeper": [[2203, "debugging-deeper"]], "TorchDynamo": [[2203, "torchdynamo"], [11, "torchdynamo"]], "Logging what Dynamo is tracing": [[2203, "logging-what-dynamo-is-tracing"]], "Breakpointing Dynamo tracing": [[2203, "breakpointing-dynamo-tracing"]], "Bytecode generation errors": [[2203, "bytecode-generation-errors"]], "AOTAutograd": [[2203, "aotautograd"]], "Summary of TORCH_LOGS options": [[2203, "summary-of-torch-logs-options"]], "Related Articles": [[2203, "related-articles"]], "AOTInductor: Ahead-Of-Time Compilation for Torch.Export-ed Models": [[2184, "aotinductor-ahead-of-time-compilation-for-torch-export-ed-models"]], "Model Compilation": [[2184, "model-compilation"]], "Inference in Python": [[2184, "inference-in-python"]], "Inference in C++": [[2184, "inference-in-c"]], "Troubleshooting": [[2184, "troubleshooting"], [2121, "troubleshooting"]], "Debugging Tools": [[2184, null], [36, "debugging-tools"]], "Dynamic shapes": [[2190, "dynamic-shapes"]], "Motivation": [[2190, "motivation"], [2193, "motivation"], [2107, "motivation"]], "Abridged public API": [[2190, "abridged-public-api"]], "The Guard Model": [[2190, "the-guard-model"]], "Overall architecture": [[2190, "overall-architecture"], [2193, "overall-architecture"]], "Abridged internal API": [[2190, "abridged-internal-api"]], "DimDynamic policy": [[2190, "dimdynamic-policy"]], "Unbacked SymInts": [[2190, "unbacked-symints"]], "torch.overrides": [[2205, "module-torch.overrides"]], "Functions": [[2205, "functions"], [2099, "functions"], [2097, "functions"], [2171, "functions"], [2155, "functions"]], "Getting Started": [[2196, "getting-started"], [8, "getting-started"]], "Using a pretrained model": [[2196, "using-a-pretrained-model"]], "Next Steps": [[2196, "next-steps"]], "Type Info": [[2209, "type-info"]], "torch.finfo": [[2209, "torch-finfo"]], "torch.iinfo": [[2209, "torch-iinfo"]], "torch.utils": [[2210, "module-torch.utils"]], "Profiling to understand torch.compile performance": [[2201, "profiling-to-understand-torch-compile-performance"]], "What to use torch.profiler for:": [[2201, "what-to-use-torch-profiler-for"]], "Basics of using torch.profiler and viewing traces": [[2201, "basics-of-using-torch-profiler-and-viewing-traces"]], "Working around CUDA Graph profiling issues": [[2201, "working-around-cuda-graph-profiling-issues"]], "Understanding compilation time": [[2201, "understanding-compilation-time"]], "Finding graph breaks: \u201cTorch-Compiled Region\u201d and \u201cCompiledFunction\u201d": [[2201, "finding-graph-breaks-torch-compiled-region-and-compiledfunction"]], "Operator Kernels": [[2201, "operator-kernels"]], "Launch overhead": [[2201, "launch-overhead"]], "CUDAGraph Trees": [[2188, "cudagraph-trees"]], "Background": [[2188, "background"], [2166, 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[[2204, "torchdynamo-errors"]], "Diagnosing TorchInductor Errors": [[2204, "diagnosing-torchinductor-errors"]], "Minifying TorchInductor Errors": [[2204, "minifying-torchinductor-errors"]], "Minifying Backend Compiler Errors": [[2204, "minifying-backend-compiler-errors"]], "Performance Profiling": [[2204, "performance-profiling"]], "Accessing TorchDynamo Profiler": [[2204, "accessing-torchdynamo-profiler"]], "TorchInductor Debugging using TORCH_COMPILE_DEBUG": [[2204, "torchinductor-debugging-using-torch-compile-debug"]], "Identifying the Cause of a Graph Break": [[2204, "identifying-the-cause-of-a-graph-break"]], "Excessive Recompilation": [[2204, "excessive-recompilation"]], "Accuracy Debugging": [[2204, "accuracy-debugging"]], "Extended Debugging": [[2204, "extended-debugging"]], "Cold Start Timing and Cache Corruption Debugging": [[2204, "cold-start-timing-and-cache-corruption-debugging"]], "PYTORCH ProcessGroupNCCL Environment Variables": [[2208, 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"torch._dynamo.disallow_in_graph": [[2195, "torch-dynamo-disallow-in-graph"]], "torch.compiler.allow_in_graph": [[2195, "torch-compiler-allow-in-graph"], [1003, "torch-compiler-allow-in-graph"]], "Fake tensor": [[2193, "fake-tensor"]], "Related work": [[2193, "related-work"]], "API: the important bits": [[2193, "api-the-important-bits"]], "Details": [[2193, "details"], [2126, "details"]], "About the tensor subclass": [[2193, "about-the-tensor-subclass"]], "How is each individual operator implemented?": [[2193, "how-is-each-individual-operator-implemented"]], "How does the converter work?": [[2193, "how-does-the-converter-work"]], "Performance characteristics": [[2193, "performance-characteristics"]], "Fake tensor of fake tensor?": [[2193, "fake-tensor-of-fake-tensor"]], "Interaction with dynamic shapes": [[2193, "interaction-with-dynamic-shapes"]], "Other resources": [[2193, "other-resources"]], "Torch Environment Variables": [[2207, "torch-environment-variables"]], "torch.xpu": 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"CausalBias": [[2124, "causalbias"]], "torch.xpu.synchronize": [[2092, "torch-xpu-synchronize"]], "torch.xpu.seed": [[2085, "torch-xpu-seed"]], "torch.xpu.stream": [[2091, "torch-xpu-stream"]], "Named Tensors": [[2120, "named-tensors"]], "Creating named tensors": [[2120, "creating-named-tensors"]], "Named dimensions": [[2120, "named-dimensions"]], "Name propagation semantics": [[2120, "name-propagation-semantics"]], "match semantics": [[2120, "match-semantics"]], "Basic name inference rules": [[2120, "basic-name-inference-rules"]], "Explicit alignment by names": [[2120, "explicit-alignment-by-names"]], "Manipulating dimensions": [[2120, "manipulating-dimensions"]], "Autograd support": [[2120, "autograd-support"]], "Currently supported operations and subsystems": [[2120, "currently-supported-operations-and-subsystems"]], "Operators": [[2120, "operators"]], "Subsystems": [[2120, "subsystems"]], "Named tensor API reference": [[2120, "named-tensor-api-reference"]], "torch.zeros": [[2093, 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"Custom Class": [[2100, "custom-class"]], "Enum Type": [[2100, "enum-type"]], "TorchScript Module Class": [[2100, "torchscript-module-class"]], "Module Instance Class": [[2100, "module-instance-class"]], "Type Annotation": [[2100, "type-annotation"]], "When to Annotate Types": [[2100, "when-to-annotate-types"]], "Annotate Function Signature": [[2100, "annotate-function-signature"]], "Annotate Variables and Data Attributes": [[2100, "annotate-variables-and-data-attributes"]], "Local Variables": [[2100, "local-variables"]], "Instance Data Attributes": [[2100, "instance-data-attributes"]], "Type Annotation APIs": [[2100, "type-annotation-apis"]], "torch.jit.annotate(T, expr)": [[2100, "torch-jit-annotate-t-expr"]], "Type Annotation Appendix": [[2100, "type-annotation-appendix"]], "TorchScript Type System Definition": [[2100, "torchscript-type-system-definition"]], "Arithmetic Conversions": [[2100, "arithmetic-conversions"]], "Atoms": [[2100, "atoms"]], "Identifiers": [[2100, "identifiers"]], "Parenthesized Forms": [[2100, "parenthesized-forms"]], "List and Dictionary Displays": [[2100, "list-and-dictionary-displays"]], "Primaries": [[2100, "primaries"]], "Attribute References": [[2100, "attribute-references"]], "Subscriptions": [[2100, "subscriptions"]], "Slicings": [[2100, "slicings"]], "Calls": [[2100, "calls"]], "Power Operator": [[2100, "power-operator"]], "Unary and Arithmetic Bitwise Operations": [[2100, "unary-and-arithmetic-bitwise-operations"]], "Binary Arithmetic Operations": [[2100, "binary-arithmetic-operations"]], "Shifting Operations": [[2100, "shifting-operations"]], "Binary Bitwise Operations": [[2100, "binary-bitwise-operations"]], "Value Comparisons": [[2100, "value-comparisons"]], "Membership Test Operations": [[2100, "membership-test-operations"]], "Identity Comparisons": [[2100, "identity-comparisons"]], "Boolean Operations": [[2100, "boolean-operations"]], "Conditional Expressions": [[2100, "conditional-expressions"]], "Expression 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"Ternary if/else Statement": [[2100, "ternary-if-else-statement"]], "The while Statement": [[2100, "the-while-statement"]], "The for-in Statement": [[2100, "the-for-in-statement"]], "The with Statement": [[2100, "the-with-statement"]], "The tuple Statement": [[2100, "the-tuple-statement"]], "The getattr Statement": [[2100, "the-getattr-statement"]], "The hasattr Statement": [[2100, "the-hasattr-statement"]], "The zip Statement": [[2100, "the-zip-statement"]], "The enumerate Statement": [[2100, "the-enumerate-statement"]], "Python Values": [[2100, "python-values"]], "Resolution Rules": [[2100, "resolution-rules"]], "Python Built-in Functions Support": [[2100, "python-built-in-functions-support"]], "TorchScript Support for Python Built-in Functions": [[2100, "id5"]], "Python Built-in Values Support": [[2100, "python-built-in-values-support"]], "TorchScript Support for Python Built-in Values": [[2100, "id6"]], "torch.* APIs": [[2100, "torch-apis"]], "Remote Procedure Calls": [[2100, "remote-procedure-calls"]], "Asynchronous Execution": [[2100, "asynchronous-execution"]], "Type Annotations": [[2100, "type-annotations"]], "Meta Programming": [[2100, "meta-programming"]], "Type Refinement": [[2100, "type-refinement"]], "torch.library": [[2104, "module-torch.library"]], "Testing custom ops": [[2104, "testing-custom-ops"]], "Creating new custom ops in Python": [[2104, "creating-new-custom-ops-in-python"]], "Extending custom ops (created from Python or C++)": [[2104, "extending-custom-ops-created-from-python-or-c"]], "Low-level APIs": [[2104, "low-level-apis"]], "torch.xpu.set_rng_state": [[2088, "torch-xpu-set-rng-state"]], "torch.utils.model_zoo": [[2111, "torch-utils-model-zoo"]], "torch.nn.functional": [[2127, "torch-nn-functional"]], "Convolution functions": [[2127, "convolution-functions"]], "Pooling functions": [[2127, "pooling-functions"]], "Attention Mechanisms": [[2127, "attention-mechanisms"]], "Non-linear activation functions": [[2127, 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"Normalization Layers": [[2122, "normalization-layers"]], "Recurrent Layers": [[2122, "recurrent-layers"]], "Transformer Layers": [[2122, "transformer-layers"]], "Linear Layers": [[2122, "linear-layers"]], "Dropout Layers": [[2122, "dropout-layers"]], "Sparse Layers": [[2122, "sparse-layers"]], "Distance Functions": [[2122, "distance-functions"]], "Loss Functions": [[2122, "loss-functions"]], "Vision Layers": [[2122, "vision-layers"]], "Shuffle Layers": [[2122, "shuffle-layers"]], "DataParallel Layers (multi-GPU, distributed)": [[2122, "module-torch.nn.parallel"]], "Utilities": [[2122, "module-torch.nn.utils"], [2179, "utilities"]], "Quantized Functions": [[2122, "quantized-functions"]], "Lazy Modules Initialization": [[2122, "lazy-modules-initialization"]], "Aliases": [[2122, "aliases"]], "torch.nn.attention.experimental": [[2125, "module-torch.nn.attention.experimental"]], "TorchScript Builtins": [[2098, "torchscript-builtins"]], "Supported Tensor Methods": [[2098, "supported-tensor-methods"]], "Supported PyTorch Functions": [[2098, "supported-pytorch-functions"]], "TorchScript Builtin Functions": [[2098, "torchscript-builtin-functions"]], "Python Built-in Functions": [[2098, "python-built-in-functions"]], "math Module": [[2098, "math-module"]], "Autograd mechanics": [[2130, "autograd-mechanics"]], "How autograd encodes the history": [[2130, "how-autograd-encodes-the-history"]], "Saved tensors": [[2130, "saved-tensors"]], "Gradients for non-differentiable functions": [[2130, "gradients-for-non-differentiable-functions"]], "Locally disabling gradient computation": [[2130, "locally-disabling-gradient-computation"], [2179, "locally-disabling-gradient-computation"], [2, "locally-disabling-gradient-computation"]], "Setting requires_grad": [[2130, "setting-requires-grad"]], "Grad Modes": [[2130, "grad-modes"]], "Default Mode (Grad Mode)": [[2130, "default-mode-grad-mode"]], "No-grad Mode": [[2130, "no-grad-mode"]], "Inference Mode": [[2130, "inference-mode"]], "Evaluation Mode (nn.Module.eval())": [[2130, "evaluation-mode-nn-module-eval"]], "In-place operations with autograd": [[2130, "in-place-operations-with-autograd"]], "In-place correctness checks": [[2130, "in-place-correctness-checks"], [2, "in-place-correctness-checks"]], "Multithreaded Autograd": [[2130, "multithreaded-autograd"]], "Concurrency on CPU": [[2130, "concurrency-on-cpu"]], "Non-determinism": [[2130, "non-determinism"]], "Graph retaining": [[2130, "graph-retaining"]], "Thread Safety on Autograd Node": [[2130, "thread-safety-on-autograd-node"]], "No thread safety on C++ hooks": [[2130, "no-thread-safety-on-c-hooks"]], "Autograd for Complex Numbers": [[2130, "autograd-for-complex-numbers"]], "What are complex derivatives?": [[2130, "what-are-complex-derivatives"]], "Wirtinger Calculus comes into the picture \u2026": [[2130, "wirtinger-calculus-comes-into-the-picture"]], "How is Wirtinger Calculus useful in optimization?": [[2130, "how-is-wirtinger-calculus-useful-in-optimization"]], "How does PyTorch compute the conjugate Wirtinger derivative?": [[2130, "how-does-pytorch-compute-the-conjugate-wirtinger-derivative"]], "How can I write my own derivative formula for a complex function?": [[2130, "how-can-i-write-my-own-derivative-formula-for-a-complex-function"]], "What about cross-domain functions?": [[2130, "what-about-cross-domain-functions"]], "Hooks for saved tensors": [[2130, "hooks-for-saved-tensors"]], "Registering hooks for a saved tensor": [[2130, "registering-hooks-for-a-saved-tensor"]], "Registering default hooks for saved tensors": [[2130, "registering-default-hooks-for-saved-tensors"]], "Backward Hooks execution": [[2130, "backward-hooks-execution"]], "Whether a particular hook will be fired": [[2130, "whether-a-particular-hook-will-be-fired"]], "The order in which the different hooks are fired": [[2130, "the-order-in-which-the-different-hooks-are-fired"]], "Special hooks": [[2130, "special-hooks"]], "Behavior of Tensor hooks when Tensor is modified in-place": [[2130, "behavior-of-tensor-hooks-when-tensor-is-modified-in-place"]], "Miscellaneous Environment Variables": [[2109, "miscellaneous-environment-variables"]], "torch.xpu.reset_peak_memory_stats": [[2084, "torch-xpu-reset-peak-memory-stats"]], "torch.mtia": [[2116, "torch-mtia"]], "torch.mps": [[2114, "module-torch.mps"]], "MPS Profiler": [[2114, "mps-profiler"]], "MPS Event": [[2114, "mps-event"]], "Multiprocessing package - torch.multiprocessing": [[2118, "module-torch.multiprocessing"]], "Strategy management": [[2118, "strategy-management"]], "Sharing CUDA tensors": [[2118, "sharing-cuda-tensors"]], "Sharing strategies": [[2118, "sharing-strategies"]], "File descriptor - file_descriptor": [[2118, "file-descriptor-file-descriptor"]], "File system - file_system": [[2118, "file-system-file-system"]], "Spawning subprocesses": [[2118, "spawning-subprocesses"]], "LPPool3d": [[1551, "lppool3d"]], "KLDivLoss": [[1547, "kldivloss"]], "EmbeddingBag": [[1525, "embeddingbag"], [756, "embeddingbag"]], "Dropout2d": [[1521, "dropout2d"]], "Hardtanh": [[1540, "hardtanh"]], "FeatureAlphaDropout": [[1526, "featurealphadropout"]], "InstanceNorm3d": [[1546, "instancenorm3d"], [763, "instancenorm3d"]], "ELU": [[1523, "elu"], [754, "elu"]], "DataParallel": [[1518, "dataparallel"]], "ConvTranspose1d": [[1512, "convtranspose1d"], [751, "convtranspose1d"]], "Flatten": [[1527, "flatten"]], "Fold": [[1528, "fold"]], "LazyConv1d": [[1558, "lazyconv1d"]], "LSTM": [[1552, "lstm"], [772, "lstm"], [744, "lstm"]], "LazyBatchNorm2d": [[1556, "lazybatchnorm2d"]], "InstanceNorm2d": [[1545, "instancenorm2d"], [762, "instancenorm2d"]], "Conv1d": [[1509, "conv1d"], [748, "conv1d"]], "LPPool2d": [[1550, "lppool2d"]], "Conv3d": [[1511, "conv3d"], [741, "conv3d"], [750, "conv3d"]], "ConvTranspose3d": [[1514, "convtranspose3d"], [753, "convtranspose3d"]], "GLU": [[1532, "glu"]], "InstanceNorm1d": [[1544, "instancenorm1d"], [761, "instancenorm1d"]], "ConvTranspose2d": [[1513, "convtranspose2d"], [752, "convtranspose2d"]], "Conv2d": [[1510, "conv2d"], [740, "conv2d"], [749, "conv2d"]], "CosineEmbeddingLoss": [[1515, "cosineembeddingloss"]], "LSTMCell": [[1553, "lstmcell"], [773, "lstmcell"]], "Dropout": [[1519, "dropout"]], "HingeEmbeddingLoss": [[1541, "hingeembeddingloss"]], "FractionalMaxPool3d": [[1530, "fractionalmaxpool3d"]], "LazyConv2d": [[1559, "lazyconv2d"]], "CosineSimilarity": [[1516, "cosinesimilarity"]], "Dropout3d": [[1522, "dropout3d"]], "GaussianNLLLoss": [[1535, "gaussiannllloss"]], "LazyBatchNorm1d": [[1555, "lazybatchnorm1d"]], "LayerNorm": [[1554, "layernorm"], [764, "layernorm"]], "Hardsigmoid": [[1538, "hardsigmoid"]], "GroupNorm": [[1536, "groupnorm"], [759, "groupnorm"]], "L1Loss": [[1548, "l1loss"]], "FractionalMaxPool2d": [[1529, "fractionalmaxpool2d"]], "GRU": [[1533, "gru"], [770, "gru"]], "Dropout1d": [[1520, "dropout1d"]], "CrossEntropyLoss": [[1517, "crossentropyloss"]], "LPPool1d": [[1549, "lppool1d"]], "Hardshrink": [[1537, "hardshrink"]], "LazyBatchNorm3d": [[1557, "lazybatchnorm3d"]], "GRUCell": [[1534, "grucell"], [771, "grucell"]], "Hardswish": [[1539, "hardswish"], [760, "hardswish"]], "GELU": [[1531, "gelu"]], "Embedding": [[1524, "embedding"], [755, "embedding"]], "HuberLoss": [[1542, "huberloss"]], "ConstantPad3d": [[1508, "constantpad3d"]], "Module": [[1582, "module"]], "ParameterDict": [[1592, "parameterdict"]], "RNNCell": [[1600, "rnncell"], [775, "rnncell"]], "MarginRankingLoss": [[1574, "marginrankingloss"]], "Sequential": [[1611, "sequential"]], "LazyInstanceNorm1d": [[1564, "lazyinstancenorm1d"]], "ReflectionPad1d": [[1604, "reflectionpad1d"]], "PReLU": [[1590, "prelu"]], "PixelUnshuffle": [[1595, "pixelunshuffle"]], "Linear": [[1569, "linear"], [766, "linear"], [774, "linear"], [742, "linear"], [743, "linear"]], "ReflectionPad2d": [[1605, "reflectionpad2d"]], "LazyConvTranspose1d": [[1561, "lazyconvtranspose1d"]], "LazyInstanceNorm3d": [[1566, "lazyinstancenorm3d"]], "ReplicationPad1d": [[1607, "replicationpad1d"]], "MaxUnpool1d": [[1578, "maxunpool1d"]], "SELU": [[1610, "selu"]], "PoissonNLLLoss": [[1596, "poissonnllloss"]], "MaxPool2d": [[1576, "maxpool2d"]], "MultiLabelMarginLoss": [[1585, "multilabelmarginloss"]], "MultiheadAttention": [[1588, "multiheadattention"], [745, "multiheadattention"]], "MaxUnpool2d": [[1579, "maxunpool2d"]], "LogSigmoid": [[1571, "logsigmoid"]], "MultiLabelSoftMarginLoss": [[1586, "multilabelsoftmarginloss"]], "MaxPool3d": [[1577, "maxpool3d"]], "MaxPool1d": [[1575, "maxpool1d"]], "LazyLinear": [[1567, "lazylinear"]], "NLLLoss": [[1589, "nllloss"]], "ReplicationPad3d": [[1609, "replicationpad3d"]], "ReplicationPad2d": [[1608, "replicationpad2d"]], "PairwiseDistance": [[1591, "pairwisedistance"]], "MaxUnpool3d": [[1580, "maxunpool3d"]], "PixelShuffle": [[1594, "pixelshuffle"]], "ParameterList": [[1593, "parameterlist"]], "LocalResponseNorm": [[1570, 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"linearrelu"], [730, "linearrelu"], [721, "linearrelu"]], "avg_pool2d": [[778, "avg-pool2d"]], "adaptive_avg_pool2d": [[776, "adaptive-avg-pool2d"]], "BNReLU2d": [[733, "bnrelu2d"], [710, "bnrelu2d"]], "freeze_bn_stats": [[731, "freeze-bn-stats"]], "Understanding TorchDynamo-based ONNX Exporter Memory Usage": [[2153, "understanding-torchdynamo-based-onnx-exporter-memory-usage"]], "TorchScript-based exporter": [[2153, "torchscript-based-exporter"]], "TorchDynamo-based exporter": [[2153, "torchdynamo-based-exporter"]], "torch.Size": [[2169, "torch-size"]], "Windows FAQ": [[2150, "windows-faq"]], "Building from source": [[2150, "building-from-source"]], "Include optional components": [[2150, "include-optional-components"]], "Speeding CUDA build for Windows": [[2150, "speeding-cuda-build-for-windows"]], "One key install script": [[2150, "one-key-install-script"]], "Extension": [[2150, "extension"]], "CFFI Extension": [[2150, "cffi-extension"]], "Cpp Extension": [[2150, "cpp-extension"]], "Installation": [[2150, "installation"], [2140, "installation"]], "Package not found in win-32 channel.": [[2150, "package-not-found-in-win-32-channel"]], "Import error": [[2150, "import-error"]], "Usage (multiprocessing)": [[2150, "usage-multiprocessing"]], "Multiprocessing error without if-clause protection": [[2150, "multiprocessing-error-without-if-clause-protection"]], "Multiprocessing error \u201cBroken pipe\u201d": [[2150, "multiprocessing-error-broken-pipe"]], "Multiprocessing error \u201cdriver shut down\u201d": [[2150, "multiprocessing-error-driver-shut-down"]], "CUDA IPC operations": [[2150, "cuda-ipc-operations"]], "torch.compiler": [[2182, "torch-compiler"]], "Getting Started for PyTorch Users": [[2182, null]], "HowTo for PyTorch Backend Vendors": [[2182, null]], "HIP (ROCm) semantics": [[2142, "hip-rocm-semantics"]], "HIP Interfaces Reuse the CUDA Interfaces": [[2142, "hip-interfaces-reuse-the-cuda-interfaces"]], "Checking for HIP": [[2142, "checking-for-hip"]], "TensorFloat-32(TF32) on ROCm": [[2142, "tensorfloat-32-tf32-on-rocm"]], "hipBLAS workspaces": [[2142, "hipblas-workspaces"]], "hipFFT/rocFFT plan cache": [[2142, "hipfft-rocfft-plan-cache"]], "torch.distributed backends": [[2142, "torch-distributed-backends"]], "CUDA API to HIP API mappings in C++": [[2142, "cuda-api-to-hip-api-mappings-in-c"]], "Refer to CUDA Semantics doc": [[2142, "refer-to-cuda-semantics-doc"]], "Enabling kernel asserts": [[2142, "enabling-kernel-asserts"]], "Quantization Backend Configuration": [[2162, "quantization-backend-configuration"], [2160, "quantization-backend-configuration"]], "Default values for native configurations": [[2162, "default-values-for-native-configurations"]], "torch.compiler.config": [[2183, "module-torch.compiler.config"]], "CUDA semantics": [[2133, "cuda-semantics"]], "TensorFloat-32 (TF32) on Ampere (and later) devices": [[2133, "tensorfloat-32-tf32-on-ampere-and-later-devices"]], "Reduced Precision Reduction in FP16 GEMMs": [[2133, "reduced-precision-reduction-in-fp16-gemms"]], "Reduced Precision Reduction in BF16 GEMMs": [[2133, "reduced-precision-reduction-in-bf16-gemms"]], "Asynchronous execution": [[2133, "asynchronous-execution"]], "CUDA streams": [[2133, "cuda-streams"]], "Stream semantics of backward passes": [[2133, "stream-semantics-of-backward-passes"]], "BC note: Using grads on the default stream": [[2133, "bc-note-using-grads-on-the-default-stream"]], "Optimizing memory usage  with PYTORCH_CUDA_ALLOC_CONF": [[2133, "optimizing-memory-usage-with-pytorch-cuda-alloc-conf"]], "Using custom memory allocators for CUDA": [[2133, "using-custom-memory-allocators-for-cuda"]], "cuBLAS workspaces": [[2133, "cublas-workspaces"]], "cuFFT plan cache": [[2133, "cufft-plan-cache"]], "Just-in-Time Compilation": [[2133, "just-in-time-compilation"]], "Best practices": [[2133, "best-practices"]], "Device-agnostic code": [[2133, "device-agnostic-code"]], "Use pinned memory buffers": [[2133, "use-pinned-memory-buffers"]], "Use nn.parallel.DistributedDataParallel instead of multiprocessing or nn.DataParallel": [[2133, "use-nn-parallel-distributeddataparallel-instead-of-multiprocessing-or-nn-dataparallel"]], "CUDA Graphs": [[2133, "cuda-graphs"]], "Why CUDA Graphs?": [[2133, "why-cuda-graphs"]], "PyTorch API": [[2133, "pytorch-api"]], "Constraints": [[2133, "constraints"], [38, "module-torch.distributions.constraints"]], "Non-constraints": [[2133, "non-constraints"]], "Whole-network capture": [[2133, "whole-network-capture"]], "Partial-network capture": [[2133, "partial-network-capture"]], "Usage with torch.cuda.amp": [[2133, "usage-with-torch-cuda-amp"]], "Usage with multiple streams": [[2133, "usage-with-multiple-streams"]], "Usage with DistributedDataParallel": [[2133, "usage-with-distributeddataparallel"]], "NCCL < 2.9.6": [[2133, "nccl-2-9-6"]], "NCCL >= 2.9.6": [[2133, "id5"]], "Graph memory management": [[2133, "graph-memory-management"]], "Sharing memory across captures": [[2133, "sharing-memory-across-captures"]], "Reproducibility": [[2148, "reproducibility"]], "Controlling sources of randomness": [[2148, "controlling-sources-of-randomness"]], "PyTorch random number generator": [[2148, "pytorch-random-number-generator"]], "Python": [[2148, "python"]], "Random number generators in other libraries": [[2148, "random-number-generators-in-other-libraries"]], "CUDA convolution benchmarking": [[2148, "cuda-convolution-benchmarking"]], "Avoiding nondeterministic algorithms": [[2148, "avoiding-nondeterministic-algorithms"]], "CUDA convolution determinism": [[2148, "cuda-convolution-determinism"]], "CUDA RNN and LSTM": [[2148, "cuda-rnn-and-lstm"]], "Filling uninitialized memory": [[2148, "filling-uninitialized-memory"]], "DataLoader": [[2148, "dataloader"]], "MPS backend": [[2145, "mps-backend"]], "torch.utils.tensorboard": [[2175, "module-torch.utils.tensorboard"]], "Extending torch.func with autograd.Function": [[2137, "extending-torch-func-with-autograd-function"]], "Basic Usage": [[2137, "basic-usage"]], "Example 1: autograd.Function calls into another system": [[2137, "example-1-autograd-function-calls-into-another-system"]], "Example 2: autograd.Function specifies custom gradient rules": [[2137, "example-2-autograd-function-specifies-custom-gradient-rules"]], "Limitations and gotchas": [[2137, "limitations-and-gotchas"]], "torch.vmap() Support": [[2137, "torch-vmap-support"]], "Automatically generate a vmap rule": [[2137, "automatically-generate-a-vmap-rule"]], "Defining the vmap staticmethod": [[2137, "defining-the-vmap-staticmethod"]], "torch.func.jvp() Support": [[2137, "torch-func-jvp-support"]], "torch.onnx": [[2151, "torch-onnx"]], "TorchDynamo-based ONNX Exporter": [[2151, "torchdynamo-based-onnx-exporter"], [2152, "torchdynamo-based-onnx-exporter"]], "TorchScript-based ONNX Exporter": [[2151, "torchscript-based-onnx-exporter"], [2155, "torchscript-based-onnx-exporter"]], "Contributing / Developing": [[2151, "contributing-developing"]], "Serialization semantics": [[2149, "serialization-semantics"]], "Table of Contents": [[2149, "table-of-contents"]], "Saving and loading tensors": [[2149, "saving-and-loading-tensors"]], "Saving and loading tensors preserves views": [[2149, "saving-and-loading-tensors-preserves-views"]], "Saving and loading torch.nn.Modules": [[2149, "saving-and-loading-torch-nn-modules"]], "Serialized file format for torch.save": [[2149, "serialized-file-format-for-torch-save"]], "torch.load with weights_only=True": [[2149, "torch-load-with-weights-only-true"]], "Troubleshooting weights_only": [[2149, "troubleshooting-weights-only"]], "Getting unsafe globals": [[2149, "getting-unsafe-globals"]], "Environment Variables": [[2149, "environment-variables"], [51, "environment-variables"]], "Serializing torch.nn.Modules and loading them in C++": [[2149, "serializing-torch-nn-modules-and-loading-them-in-c"]], "Saving and loading ScriptModules across PyTorch versions": [[2149, "saving-and-loading-scriptmodules-across-pytorch-versions"]], "torch.div performing integer division": [[2149, "torch-div-performing-integer-division"]], "torch.full always inferring a float dtype": [[2149, "torch-full-always-inferring-a-float-dtype"]], "Utility functions": [[2149, "utility-functions"], [2163, "utility-functions"]], "Config": [[2149, "module-torch.utils.serialization"]], "Distributed Autograd Design": [[2166, "distributed-autograd-design"]], "Autograd recording during the forward pass": [[2166, "autograd-recording-during-the-forward-pass"]], "Distributed Autograd Context": [[2166, "distributed-autograd-context"]], "Distributed Backward Pass": [[2166, "distributed-backward-pass"]], "Computing dependencies": [[2166, "computing-dependencies"]], "FAST mode algorithm": [[2166, "fast-mode-algorithm"]], "SMART mode algorithm": [[2166, "smart-mode-algorithm"]], "Distributed Optimizer": [[2166, "distributed-optimizer"], [2165, "distributed-optimizer"]], "Simple end to end example": [[2166, "simple-end-to-end-example"]], "torch.special": [[2171, "torch-special"]], "Getting Started on Intel GPU": [[2140, "getting-started-on-intel-gpu"]], "Hardware Prerequisite": [[2140, "hardware-prerequisite"]], "Software Prerequisite": [[2140, "software-prerequisite"]], "Binaries": [[2140, "binaries"]], "From Source": [[2140, "from-source"]], "Check availability for Intel GPU": [[2140, "check-availability-for-intel-gpu"]], "Minimum Code Change": [[2140, "minimum-code-change"]], "Inference Examples": [[2140, "inference-examples"]], "Inference with FP32": [[2140, "inference-with-fp32"]], "Inference with AMP": [[2140, "inference-with-amp"]], "Inference with torch.compile": [[2140, "inference-with-torch-compile"]], "Training Examples": [[2140, "training-examples"]], "Train with FP32": [[2140, "train-with-fp32"]], "Train with AMP": [[2140, "train-with-amp"]], "Train with torch.compile": [[2140, "train-with-torch-compile"]], "torch.package": [[2158, "torch-package"]], "Tutorials": [[2158, "tutorials"], [2165, "tutorials"], [8, "tutorials"]], "Packaging your first model": [[2158, "packaging-your-first-model"]], "How do I\u2026": [[2158, "how-do-i"]], "See what is inside a package?": [[2158, "see-what-is-inside-a-package"]], "Treat the package like a ZIP archive": [[2158, "treat-the-package-like-a-zip-archive"]], "Use the file_structure() API": [[2158, "use-the-file-structure-api"]], "See why a given module was included as a dependency?": [[2158, "see-why-a-given-module-was-included-as-a-dependency"]], "Include arbitrary resources with my package and access them later?": [[2158, "include-arbitrary-resources-with-my-package-and-access-them-later"]], "Customize how a class is packaged?": [[2158, "customize-how-a-class-is-packaged"]], "Test in my source code whether or not it is executing inside a package?": [[2158, "test-in-my-source-code-whether-or-not-it-is-executing-inside-a-package"]], "Patch code into a package?": [[2158, "patch-code-into-a-package"]], "Access package contents from packaged code?": [[2158, "access-package-contents-from-packaged-code"]], "Distinguish between packaged code and non-packaged code?": [[2158, "distinguish-between-packaged-code-and-non-packaged-code"]], "Re-export an imported object?": [[2158, "re-export-an-imported-object"]], "Package a TorchScript module?": [[2158, "package-a-torchscript-module"]], "Explanation": [[2158, "explanation"]], "torch.package Format Overview": [[2158, "torch-package-format-overview"]], "Framework files": [[2158, "framework-files"]], "User files": [[2158, "user-files"]], "How torch.package finds your code\u2019s dependencies": [[2158, "how-torch-package-finds-your-code-s-dependencies"]], "Analyzing an object\u2019s dependencies": [[2158, "analyzing-an-object-s-dependencies"]], "Analyzing a module\u2019s dependencies": [[2158, "analyzing-a-module-s-dependencies"]], "Dependency Management": [[2158, "dependency-management"]], "intern": [[2158, "intern"]], "extern": [[2158, "extern"]], "mock": [[2158, "mock"]], "Refactoring": [[2158, "refactoring"]], "Patterns": [[2158, "patterns"]], "torch.package sharp edges": [[2158, "torch-package-sharp-edges"]], "Avoid global state in your modules": [[2158, "avoid-global-state-in-your-modules"]], "Types are not shared between packages and the loading environment": [[2158, "types-are-not-shared-between-packages-and-the-loading-environment"]], "How torch.package keeps packages isolated from each other": [[2158, "how-torch-package-keeps-packages-isolated-from-each-other"]], "Mangling": [[2158, "mangling"]], "torch.testing": [[2177, "module-torch.testing"]], "Quantization": [[2160, "module-torch.ao.quantization"]], "Introduction to Quantization": [[2160, "introduction-to-quantization"]], "Quantization API Summary": [[2160, "quantization-api-summary"]], "Eager Mode Quantization": [[2160, "eager-mode-quantization"]], "Post Training Dynamic Quantization": [[2160, "post-training-dynamic-quantization"]], "Post Training Static Quantization": [[2160, "post-training-static-quantization"]], "Quantization Aware Training for Static Quantization": [[2160, "quantization-aware-training-for-static-quantization"]], "Model Preparation for Eager Mode Static Quantization": [[2160, "model-preparation-for-eager-mode-static-quantization"]], "(Prototype - maintenance mode) FX Graph Mode Quantization": [[2160, "prototype-maintenance-mode-fx-graph-mode-quantization"]], "(Prototype) PyTorch 2 Export Quantization": [[2160, "prototype-pytorch-2-export-quantization"]], "Quantization Stack": [[2160, "quantization-stack"]], "Quantized Model": [[2160, "quantized-model"]], "Quantized Tensor": [[2160, "quantized-tensor"]], "Quantize and Dequantize": [[2160, "quantize-and-dequantize"]], "Quantized Operators/Modules": [[2160, "quantized-operators-modules"]], "Quantized Engine": [[2160, "quantized-engine"]], "Quantization Flow": [[2160, "quantization-flow"]], "Observer and FakeQuantize": [[2160, "observer-and-fakequantize"]], "General Quantization Flow": [[2160, "general-quantization-flow"]], "Quantization Support Matrix": [[2160, "quantization-support-matrix"]], "Quantization Mode Support": [[2160, "quantization-mode-support"]], "Quantization Flow Support": [[2160, "quantization-flow-support"]], "Backend/Hardware Support": [[2160, "backend-hardware-support"]], "Note for native CPU backends": [[2160, "note-for-native-cpu-backends"]], "Operator Support": [[2160, "operator-support"]], "Quantization API Reference": [[2160, "quantization-api-reference"], [2163, "quantization-api-reference"]], "Quantization Accuracy Debugging": [[2160, "quantization-accuracy-debugging"], [2161, "quantization-accuracy-debugging"]], "Quantization Customizations": [[2160, "quantization-customizations"]], "Quantization Custom Module API": [[2160, "quantization-custom-module-api"]], "Best Practices": [[2160, "best-practices"]], "Common Errors": [[2160, "common-errors"]], "Passing a non-quantized Tensor into a quantized kernel": [[2160, "passing-a-non-quantized-tensor-into-a-quantized-kernel"]], "Passing a quantized Tensor into a non-quantized kernel": [[2160, "passing-a-quantized-tensor-into-a-non-quantized-kernel"]], "Saving and Loading Quantized models": [[2160, "saving-and-loading-quantized-models"]], "Symbolic Trace Error when using FX Graph Mode Quantization": [[2160, "symbolic-trace-error-when-using-fx-graph-mode-quantization"]], "Extending PyTorch": [[2136, "extending-pytorch"]], "Adding new operators": [[2136, "adding-new-operators"]], "Extending torch.autograd": [[2136, "extending-torch-autograd"]], "When to use": [[2136, "when-to-use"]], "When not to use": [[2136, "when-not-to-use"]], "How to use": [[2136, "how-to-use"]], "Example": [[2136, "example"], [2135, "example"]], "Combined or separate forward() and setup_context()": [[2136, "combined-or-separate-forward-and-setup-context"]], "Forward mode AD": [[2136, "forward-mode-ad"]], "torch.func transforms and/or torch.vmap()": [[2136, "torch-func-transforms-and-or-torch-vmap"]], "Extending torch.nn": [[2136, "extending-torch-nn"]], "Adding a Module": [[2136, "adding-a-module"]], "Extending torch Python API": [[2136, "extending-torch-python-api"]], "Extending torch with a Tensor-like type": [[2136, "extending-torch-with-a-tensor-like-type"]], "Subclassing torch.Tensor": [[2136, "subclassing-torch-tensor"]], "Extending torch with a Tensor wrapper type": [[2136, "extending-torch-with-a-tensor-wrapper-type"]], "Operations on multiple types that define __torch_function__": [[2136, "operations-on-multiple-types-that-define-torch-function"]], "Testing Coverage of Overrides for the PyTorch API": [[2136, "testing-coverage-of-overrides-for-the-pytorch-api"]], "Extending torch native API": [[2136, "extending-torch-native-api"]], "__torch_dispatch__ calling convention": [[2136, "torch-dispatch-calling-convention"]], "Extending all torch API with Modes": [[2136, 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"name": [2, 3, 4, 15, 20, 25, 29, 31, 33, 35, 37, 38, 40, 43, 44, 47, 48, 50, 53, 55, 56, 59, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 680, 707, 757, 758, 767, 804, 828, 829, 830, 831, 842, 868, 883, 887, 961, 964, 966, 1011, 1013, 1056, 1069, 1197, 1202, 1212, 1220, 1222, 1227, 1229, 1313, 1315, 1316, 1323, 1326, 1331, 1332, 1346, 1351, 1352, 1358, 1359, 1361, 1362, 1363, 1364, 1374, 1375, 1377, 1379, 1380, 1387, 1582, 1633, 1641, 1764, 1769, 1770, 1772, 1789, 1790, 1791, 1792, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1824, 1825, 1827, 1836, 1837, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1927, 1937, 2037, 2039, 2042, 2064, 2095, 2096, 2097, 2098, 2100, 2101, 2104, 2106, 2111, 2112, 2113, 2118, 2121, 2128, 2130, 2133, 2135, 2136, 2141, 2143, 2144, 2149, 2152, 2155, 2156, 2158, 2159, 2165, 2166, 2172, 2175, 2176, 2177, 2179, 2180, 2181, 2184, 2185, 2189, 2191, 2192, 2194, 2197, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2209], "factori": [2, 3, 12, 38, 40, 44, 48, 50, 64, 834, 842, 968, 1145, 1642, 1828, 1837, 1935, 2098, 2104, 2108, 2120, 2133, 2136, 2156, 2173, 2176, 2190, 2193], "ones": [2, 4, 6, 20, 24, 29, 34, 36, 37, 38, 55, 57, 59, 64, 65, 67, 68, 70, 75, 253, 312, 401, 444, 445, 447, 514, 806, 888, 922, 938, 940, 941, 942, 943, 944, 948, 971, 983, 1001, 1128, 1136, 1158, 1202, 1209, 1210, 1213, 1312, 1315, 1327, 1336, 1362, 1378, 1387, 1495, 1515, 1524, 1528, 1535, 1536, 1554, 1582, 1586, 1587, 1589, 1591, 1597, 1634, 1643, 1644, 1645, 1679, 1740, 1758, 1762, 1771, 1772, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1806, 1807, 1820, 1822, 1825, 1835, 1836, 1880, 1899, 1916, 1934, 1939, 1964, 2017, 2021, 2039, 2051, 2098, 2100, 2102, 2104, 2108, 2119, 2120, 2130, 2131, 2133, 2136, 2145, 2147, 2148, 2155, 2156, 2157, 2158, 2160, 2165, 2167, 2169, 2171, 2172, 2173, 2176, 2197, 2202, 2203, 2204], "autograd_tensor": 2, "kwarg": [2, 6, 15, 24, 29, 30, 31, 33, 34, 35, 37, 50, 55, 56, 59, 68, 70, 579, 602, 757, 770, 772, 773, 834, 836, 838, 839, 840, 842, 845, 919, 934, 935, 1005, 1040, 1041, 1042, 1044, 1086, 1087, 1105, 1202, 1213, 1214, 1216, 1227, 1229, 1315, 1318, 1320, 1379, 1405, 1543, 1571, 1582, 1613, 1617, 1621, 1623, 1624, 1762, 1772, 1797, 1802, 1806, 1816, 1823, 1825, 1828, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 1927, 2033, 2048, 2054, 2095, 2100, 2101, 2104, 2136, 2137, 2144, 2152, 2155, 2157, 2165, 2172, 2176, 2181, 2189, 2192, 2202, 2203, 2204, 2205], "base": [2, 4, 8, 10, 13, 15, 16, 21, 24, 29, 30, 31, 33, 34, 35, 36, 37, 38, 40, 43, 48, 50, 51, 53, 55, 56, 57, 59, 64, 68, 680, 803, 808, 814, 816, 834, 835, 837, 838, 839, 840, 842, 845, 848, 862, 890, 891, 929, 932, 934, 945, 946, 955, 1001, 1026, 1106, 1107, 1122, 1143, 1145, 1188, 1235, 1246, 1269, 1279, 1317, 1329, 1341, 1379, 1390, 1392, 1394, 1402, 1495, 1518, 1555, 1582, 1585, 1586, 1587, 1591, 1597, 1599, 1626, 1628, 1630, 1705, 1740, 1771, 1772, 1778, 1779, 1780, 1787, 1789, 1797, 1801, 1803, 1845, 1862, 1863, 1892, 1902, 1927, 1939, 1997, 1998, 2015, 2025, 2027, 2049, 2096, 2098, 2100, 2104, 2121, 2132, 2133, 2135, 2136, 2139, 2144, 2159, 2160, 2165, 2166, 2170, 2171, 2172, 2174, 2175, 2177, 2180, 2181, 2183, 2190, 2191, 2192, 2194, 2196, 2203], "static": [2, 4, 9, 15, 29, 30, 35, 36, 38, 40, 50, 55, 56, 59, 70, 75, 76, 79, 680, 806, 813, 829, 831, 842, 850, 858, 886, 888, 918, 919, 920, 921, 934, 935, 1042, 1222, 1223, 1229, 1235, 1254, 1329, 1626, 1772, 1971, 2097, 2098, 2099, 2100, 2133, 2139, 2143, 2149, 2151, 2152, 2161, 2165, 2172, 2188, 2190, 2192, 2203], "Then": [2, 30, 35, 68, 957, 1528, 1634, 1792, 1837, 2037, 2130, 2131, 2133, 2135, 2136, 2137, 2147, 2148, 2155, 2157, 2158, 2165, 2166, 2180, 2185, 2188, 2191, 2192, 2195], "op": [2, 5, 6, 15, 25, 29, 31, 33, 35, 36, 55, 56, 57, 59, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 149, 502, 523, 603, 689, 690, 692, 767, 804, 805, 806, 841, 850, 883, 890, 891, 922, 929, 932, 934, 935, 943, 991, 1001, 1003, 1045, 1061, 1064, 1068, 1069, 1086, 1087, 1114, 1118, 1120, 1203, 1266, 1316, 1329, 1331, 1332, 1387, 1393, 1446, 1450, 1461, 1463, 1465, 1599, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1858, 1859, 1860, 1861, 1862, 1863, 1883, 1941, 2023, 2055, 2058, 2060, 2063, 2064, 2087, 2090, 2091, 2100, 2106, 2107, 2110, 2113, 2115, 2119, 2120, 2132, 2133, 2135, 2136, 2146, 2152, 2158, 2159, 2160, 2161, 2162, 2163, 2170, 2172, 2174, 2176, 2181, 2185, 2188, 2189, 2193, 2194, 2195, 2196, 2198, 2201, 2202, 2204], "directli": [2, 4, 8, 10, 15, 16, 24, 25, 29, 31, 33, 35, 36, 37, 38, 39, 40, 49, 55, 56, 57, 59, 63, 68, 70, 83, 556, 742, 757, 837, 919, 929, 932, 934, 1003, 1014, 1128, 1196, 1203, 1210, 1212, 1225, 1259, 1346, 1588, 1616, 1635, 1699, 1746, 1817, 2021, 2097, 2099, 2100, 2104, 2108, 2113, 2121, 2130, 2133, 2135, 2136, 2137, 2139, 2141, 2143, 2144, 2145, 2146, 2149, 2158, 2159, 2160, 2165, 2170, 2172, 2175, 2177, 2184, 2190, 2192, 2193, 2194, 2196, 2197, 2202, 2204], "ctx": [2, 6, 48, 70, 74, 918, 919, 920, 929, 930, 931, 932, 933, 934, 935, 2104, 2129, 2136, 2137, 2155, 2203], "gradcheck": [2, 1970, 2096, 2104, 2136, 2170], "extend": [2, 24, 29, 31, 35, 38, 42, 53, 68, 890, 919, 921, 929, 932, 934, 935, 1584, 1593, 1970, 2037, 2096, 2130, 2143, 2145, 2146, 2152, 2155, 2156, 2158, 2163, 2170, 2190, 2191, 2205], "staticmethod": [2, 70, 919, 921, 929, 930, 931, 932, 933, 934, 935, 2100, 2129, 2136, 2155, 2165], "result": [2, 4, 5, 6, 8, 9, 10, 13, 15, 19, 20, 24, 25, 29, 31, 35, 36, 38, 40, 43, 48, 51, 55, 56, 57, 59, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 152, 220, 221, 253, 254, 314, 334, 397, 495, 497, 498, 555, 582, 583, 584, 586, 587, 617, 696, 697, 698, 699, 700, 703, 705, 706, 707, 709, 770, 805, 809, 894, 906, 908, 927, 928, 937, 938, 939, 940, 941, 942, 948, 949, 957, 969, 972, 983, 987, 992, 998, 1000, 1001, 1003, 1004, 1013, 1014, 1018, 1022, 1024, 1051, 1053, 1055, 1056, 1086, 1087, 1101, 1124, 1125, 1126, 1127, 1128, 1140, 1145, 1163, 1166, 1172, 1181, 1188, 1190, 1193, 1198, 1199, 1202, 1203, 1204, 1207, 1208, 1213, 1214, 1223, 1227, 1238, 1243, 1252, 1255, 1259, 1277, 1278, 1280, 1292, 1306, 1315, 1318, 1319, 1327, 1331, 1332, 1334, 1336, 1337, 1341, 1355, 1357, 1359, 1360, 1368, 1372, 1374, 1376, 1377, 1378, 1385, 1395, 1396, 1403, 1413, 1415, 1416, 1417, 1418, 1420, 1421, 1473, 1476, 1491, 1492, 1493, 1518, 1520, 1521, 1522, 1526, 1528, 1533, 1547, 1552, 1582, 1588, 1598, 1626, 1627, 1628, 1629, 1630, 1634, 1670, 1688, 1699, 1755, 1758, 1759, 1772, 1789, 1792, 1825, 1828, 1829, 1830, 1831, 1837, 1840, 1875, 1880, 1883, 1885, 1892, 1893, 1896, 1902, 1915, 1921, 1936, 1951, 1963, 1974, 1976, 1978, 1982, 1989, 1991, 1992, 1993, 1996, 1997, 1998, 2010, 2014, 2015, 2020, 2021, 2023, 2024, 2026, 2036, 2039, 2043, 2044, 2048, 2069, 2082, 2097, 2098, 2099, 2100, 2104, 2107, 2108, 2110, 2119, 2120, 2121, 2128, 2129, 2130, 2131, 2132, 2133, 2135, 2136, 2137, 2138, 2141, 2142, 2144, 2146, 2147, 2148, 2149, 2151, 2152, 2153, 2155, 2157, 2159, 2160, 2161, 2162, 2165, 2166, 2169, 2170, 2172, 2173, 2175, 2176, 2177, 2181, 2182, 2184, 2189, 2190, 2191, 2193, 2197, 2200, 2201, 2202, 2203, 2204, 2205], "save_for_backward": [2, 919, 929, 931, 933, 934, 935, 2104, 2129, 2130, 2136, 2137, 2155], "grad_output": [2, 70, 918, 929, 930, 934, 935, 943, 949, 953, 954, 1315, 1582, 1880, 2098, 2130, 2133, 2136, 2137, 2144, 2198], "saved_tensor": [2, 929, 931, 932, 933, 934, 935, 2104, 2129, 2130, 2136, 2137], "inspect": [2, 29, 55, 59, 68, 960, 1315, 1327, 2129, 2136, 2143, 2158, 2161, 2173, 2194, 2196, 2197, 2203, 2205], "cost": [2, 4, 5, 9, 10, 25, 31, 33, 35, 36, 59, 485, 991, 1001, 1371, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1841, 1935, 2133, 2139, 2148, 2161, 2166, 2173, 2188, 2199, 2203, 2204], "both": [2, 3, 4, 15, 20, 24, 25, 26, 29, 30, 31, 33, 38, 43, 48, 49, 50, 51, 55, 56, 57, 59, 67, 68, 70, 78, 79, 96, 338, 498, 739, 778, 779, 782, 783, 784, 805, 834, 880, 921, 928, 934, 935, 938, 939, 940, 941, 942, 969, 1026, 1064, 1101, 1140, 1146, 1147, 1148, 1161, 1191, 1192, 1193, 1202, 1257, 1259, 1269, 1276, 1292, 1305, 1306, 1312, 1315, 1329, 1337, 1338, 1386, 1402, 1410, 1416, 1434, 1435, 1491, 1492, 1496, 1497, 1498, 1506, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1536, 1542, 1544, 1545, 1546, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1575, 1576, 1577, 1582, 1588, 1622, 1630, 1634, 1635, 1638, 1655, 1656, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1713, 1714, 1715, 1740, 1772, 1783, 1785, 1795, 1825, 1841, 1843, 1844, 1862, 1875, 1880, 1900, 1901, 1939, 1944, 1969, 1971, 1973, 1993, 1997, 2020, 2021, 2060, 2097, 2099, 2100, 2104, 2107, 2119, 2120, 2121, 2122, 2126, 2129, 2130, 2131, 2136, 2137, 2139, 2140, 2141, 2144, 2147, 2148, 2149, 2151, 2155, 2157, 2158, 2160, 2163, 2164, 2165, 2167, 2170, 2171, 2172, 2177, 2180, 2181, 2182, 2188, 2190, 2192, 2193, 2194, 2195, 2196, 2200, 2203, 2204], "cpu": [2, 5, 6, 15, 18, 24, 29, 31, 33, 36, 51, 56, 59, 70, 72, 75, 79, 80, 81, 85, 86, 118, 195, 288, 325, 334, 444, 445, 446, 447, 448, 457, 485, 579, 588, 683, 894, 909, 962, 964, 970, 979, 1024, 1027, 1028, 1039, 1051, 1052, 1053, 1055, 1146, 1148, 1158, 1163, 1181, 1193, 1197, 1199, 1200, 1264, 1266, 1267, 1268, 1273, 1274, 1291, 1315, 1323, 1325, 1326, 1335, 1345, 1347, 1351, 1352, 1353, 1354, 1356, 1358, 1361, 1363, 1364, 1370, 1373, 1376, 1379, 1380, 1386, 1387, 1402, 1405, 1416, 1428, 1444, 1518, 1524, 1582, 1755, 1772, 1778, 1779, 1780, 1787, 1816, 1817, 1831, 1834, 1842, 1880, 1895, 1904, 1906, 1908, 1910, 1911, 1935, 1940, 1941, 1942, 1944, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1963, 1973, 1979, 1980, 1981, 1982, 1983, 1984, 1997, 2014, 2015, 2016, 2025, 2027, 2032, 2036, 2053, 2093, 2096, 2097, 2098, 2100, 2104, 2108, 2110, 2115, 2118, 2119, 2121, 2126, 2133, 2136, 2137, 2139, 2142, 2144, 2147, 2148, 2149, 2150, 2155, 2156, 2157, 2159, 2161, 2164, 2165, 2170, 2172, 2173, 2176, 2177, 2179, 2182, 2184, 2188, 2192, 2193, 2194, 2196, 2197, 2200, 2201, 2203, 2204], "There": [2, 6, 7, 8, 10, 13, 15, 20, 23, 29, 31, 36, 50, 55, 56, 57, 59, 60, 64, 65, 68, 680, 744, 805, 919, 934, 935, 1001, 1203, 1518, 1552, 1598, 1740, 1772, 1781, 1782, 1823, 1828, 2095, 2097, 2099, 2100, 2106, 2107, 2120, 2121, 2130, 2133, 2136, 2137, 2138, 2143, 2146, 2149, 2150, 2151, 2155, 2158, 2159, 2160, 2161, 2165, 2167, 2172, 2176, 2179, 2184, 2188, 2190, 2191, 2193, 2194, 2201, 2202, 2203, 2204], "moment": [2, 37, 70, 76, 782, 783, 784, 1022, 1841, 1843, 1844, 1845, 1847, 1859, 1863, 2110, 2118, 2159, 2165, 2200], "nvprof": [2, 5, 958, 1056, 2133], "regist": [2, 15, 16, 20, 25, 29, 33, 34, 36, 38, 50, 53, 55, 56, 57, 59, 68, 486, 487, 680, 804, 805, 883, 953, 954, 1001, 1014, 1089, 1229, 1315, 1387, 1582, 1583, 1584, 1592, 1593, 1611, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1774, 1789, 1790, 1792, 1793, 1795, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 2037, 2039, 2104, 2113, 2118, 2121, 2133, 2135, 2136, 2143, 2144, 2149, 2152, 2155, 2158, 2165, 2182, 2191, 2193, 2196, 2199], "activ": [2, 6, 8, 10, 18, 29, 33, 35, 36, 37, 53, 55, 59, 65, 804, 806, 808, 817, 821, 870, 871, 880, 890, 891, 964, 1042, 1049, 1066, 1081, 1101, 1202, 1204, 1214, 1215, 1216, 1388, 1490, 1520, 1521, 1522, 1526, 1536, 1554, 1581, 1583, 1601, 1612, 1626, 1628, 1630, 1681, 1719, 1743, 1772, 1793, 1795, 1825, 1830, 1880, 2048, 2081, 2110, 2118, 2121, 2133, 2136, 2137, 2139, 2144, 2155, 2157, 2159, 2160, 2161, 2162, 2165, 2170, 2180, 2181, 2188, 2193, 2198, 2203, 2204], "emit_nvtx": [2, 5, 1056], "vtune": [2, 5], "emit_itt": [2, 5], "use_cuda": [2, 2159], "use_devic": 2, "record_shap": 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2150, 2152, 2155, 2158, 2159, 2183, 2185, 2190, 2204], "kineto": [2, 957, 2159], "_experimentalconfig": [2, 2159], "librari": [2, 3, 4, 5, 9, 10, 12, 15, 16, 18, 20, 21, 24, 29, 39, 48, 53, 55, 57, 60, 63, 64, 65, 68, 680, 957, 1024, 1040, 1067, 1071, 1075, 1086, 1196, 1328, 1359, 1405, 1963, 2062, 2066, 2069, 2110, 2113, 2132, 2133, 2135, 2136, 2137, 2138, 2143, 2144, 2146, 2147, 2150, 2155, 2158, 2159, 2160, 2165, 2178, 2184, 2188, 2191, 2194, 2203, 2205, 2207], "functionev": [2, 2159], "across": [2, 9, 15, 20, 21, 24, 25, 29, 31, 33, 34, 35, 36, 37, 40, 51, 59, 64, 68, 85, 616, 745, 903, 962, 1041, 1055, 1100, 1101, 1145, 1159, 1214, 1326, 1331, 1417, 1444, 1495, 1518, 1528, 1570, 1588, 1590, 1622, 1634, 1658, 1661, 1690, 1706, 1755, 1772, 1802, 1806, 1830, 1880, 1927, 2018, 2048, 2081, 2096, 2097, 2113, 2119, 2126, 2130, 2135, 2138, 2143, 2144, 2146, 2147, 2148, 2157, 2159, 2161, 2165, 2166, 2172, 2175, 2181, 2183, 2188, 2190, 2194, 2204], "cycl": [2, 485, 922, 1040, 1075, 1229, 1868, 1875, 2069, 2130, 2159], "100": [2, 20, 24, 29, 34, 38, 68, 70, 78, 79, 84, 299, 692, 748, 749, 752, 753, 957, 1056, 1276, 1315, 1327, 1333, 1371, 1386, 1393, 1402, 1417, 1467, 1486, 1494, 1495, 1496, 1497, 1498, 1510, 1511, 1513, 1514, 1516, 1517, 1544, 1545, 1546, 1582, 1589, 1591, 1622, 1631, 1670, 1671, 1724, 1846, 1864, 1865, 1871, 1872, 1873, 1874, 1876, 1878, 1879, 1880, 1897, 1952, 2034, 2098, 2100, 2118, 2132, 2133, 2155, 2157, 2161, 2167, 2169, 2170, 2175, 2189, 2192, 2197, 2203], "realli": [2, 8, 55, 68, 1227, 1229, 2100, 2121, 2130, 2158, 2193], "y": [2, 6, 12, 15, 24, 36, 38, 47, 55, 56, 57, 59, 64, 65, 68, 70, 73, 75, 76, 77, 78, 79, 80, 81, 82, 485, 616, 619, 791, 913, 915, 916, 917, 927, 928, 929, 932, 934, 935, 937, 938, 939, 940, 941, 942, 944, 945, 964, 983, 989, 1019, 1026, 1056, 1086, 1087, 1128, 1139, 1145, 1149, 1202, 1204, 1207, 1208, 1209, 1213, 1214, 1255, 1322, 1327, 1331, 1384, 1393, 1394, 1417, 1494, 1495, 1496, 1497, 1498, 1499, 1515, 1517, 1536, 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2136, 2149, 2155, 2158, 2188, 2191, 2193, 2194, 2199, 2202, 2203, 2204], "remov": [2, 4, 24, 25, 29, 31, 33, 38, 51, 54, 59, 68, 486, 487, 512, 545, 556, 707, 810, 829, 830, 831, 888, 953, 954, 957, 964, 968, 991, 992, 1089, 1203, 1260, 1312, 1315, 1319, 1405, 1406, 1410, 1503, 1504, 1505, 1582, 1583, 1592, 1690, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1790, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1809, 1810, 1811, 1812, 1814, 1815, 1825, 1827, 1830, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 1895, 1911, 1933, 1988, 1997, 2023, 2030, 2032, 2095, 2110, 2121, 2122, 2130, 2136, 2144, 2149, 2155, 2156, 2158, 2167, 2172, 2177, 2191, 2194, 2196, 2199, 2202], "breviti": [2, 68, 964, 1026, 2121, 2155], "print": [2, 4, 6, 13, 19, 24, 26, 29, 35, 36, 40, 42, 47, 48, 51, 55, 56, 57, 59, 64, 67, 232, 730, 738, 739, 744, 755, 756, 766, 774, 951, 953, 954, 964, 1056, 1202, 1203, 1212, 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1939, 1968, 1971, 1979, 1980, 1981, 1983, 1984, 1985, 1993, 1997, 2016, 2018, 2021, 2046, 2047, 2099, 2100, 2108, 2113, 2119, 2130, 2133, 2136, 2141, 2148, 2152, 2158, 2159, 2170, 2176, 2177, 2189, 2191, 2197, 2203, 2204], "stdin": [2, 29, 944, 1971, 2108, 2136, 2148, 2170], "instal": [2, 3, 4, 15, 16, 29, 36, 68, 1145, 1229, 2095, 2144, 2145, 2152, 2153, 2154, 2155, 2158, 2165, 2175, 2184, 2189, 2190, 2191, 2192, 2199, 2203], "_tensor": [2, 36, 153], "py": [2, 5, 15, 19, 29, 34, 35, 38, 42, 44, 49, 51, 56, 57, 59, 68, 889, 1056, 1772, 2095, 2097, 2100, 2106, 2121, 2135, 2141, 2143, 2146, 2155, 2158, 2159, 2160, 2166, 2184, 2185, 2187, 2189, 2190, 2191, 2192, 2193, 2194, 2196, 2197, 2200, 2202, 2203, 2204, 2206], "93": [2, 616, 1127], "retain_graph": [2, 149, 922, 943, 953, 954, 1214, 2048, 2098, 2129, 2130, 2136, 2165], "90": [2, 1129, 1879, 1923, 2104, 2115], "allow_unreach": 2, "76": 2, "_forward_cl": 2, "tmp": [2, 4, 15, 29, 48, 50, 2095, 2133, 2159, 2185, 2188, 2197, 2203], "53": [2, 480], "44": [2, 320, 444, 1148, 1493, 1551, 1577, 1823, 2192], "set_detect_anomali": 2, "behaviour": [2, 55, 703, 704, 709, 1688, 1727, 1875, 1945, 2095, 2147, 2172], "interpos": [2, 2136], "grad_fn": [2, 149, 334, 487, 909, 922, 937, 938, 939, 940, 941, 942, 951, 953, 954, 1202, 1790, 1973, 2121, 2130, 2136, 2144, 2149], "node": [2, 29, 33, 34, 40, 43, 44, 49, 50, 55, 59, 68, 70, 80, 81, 680, 812, 833, 836, 838, 839, 842, 845, 850, 866, 929, 964, 1089, 1212, 1227, 1229, 1247, 1252, 1330, 1501, 1518, 1772, 1837, 2110, 2133, 2155, 2158, 2165, 2166, 2167, 2179, 2181, 2183, 2185, 2190, 2191, 2193, 2194, 2200, 2202, 2204], "grad_mod": [2, 944, 945, 946, 2098], "least": [2, 6, 7, 9, 25, 31, 33, 38, 49, 50, 59, 259, 401, 707, 834, 972, 1133, 1135, 1136, 1145, 1186, 1187, 1197, 1228, 1269, 1277, 1278, 1312, 1338, 1361, 1388, 1410, 1472, 1486, 1772, 1817, 2095, 2100, 2120, 2128, 2130, 2131, 2133, 2138, 2139, 2161, 2167, 2170, 2171, 2172, 2177, 2194, 2196, 2203], "intermediari": [2, 15, 38, 929, 932, 934, 2130, 2141, 2191, 2194], "access": [2, 10, 15, 19, 24, 29, 31, 35, 55, 56, 67, 70, 72, 487, 556, 744, 929, 932, 934, 935, 967, 1048, 1056, 1315, 1318, 1331, 1378, 1486, 1582, 1762, 1775, 1776, 1790, 1795, 1817, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 2070, 2097, 2098, 2100, 2101, 2104, 2110, 2112, 2114, 2116, 2118, 2120, 2121, 2130, 2133, 2138, 2141, 2143, 2144, 2149, 2165, 2170, 2172, 2173, 2174, 2176, 2184, 2188, 2190, 2191, 2192, 2193, 2194, 2196, 2199, 2203, 2209], "isinst": [2, 24, 38, 68, 951, 953, 954, 1302, 1313, 2098, 2100, 2130, 2136, 2144, 2157, 2158, 2170, 2194, 2202, 2204], "dir": [2, 1086, 1837, 2095, 2100, 2158], "__call__": [2, 1315, 1582, 1880, 2191], "__class__": [2, 44, 70], "__delattr__": 2, "__dir__": 2, "__doc__": 2, "__eq__": 2, "__format__": [2, 2101], "__ge__": 2, "__getattribute__": 2, "__gt__": 2, "__hash__": [2, 2101], "__init_subclass__": 2, "__le__": 2, "__lt__": [2, 2100], "__ne__": 2, "__new__": [2, 2099, 2101], "__reduce__": [2, 2158], "__reduce_ex__": 2, "__repr__": [2, 4, 2136], "__setattr__": 2, "__sizeof__": 2, "__str__": [2, 68, 2098, 2100], "__subclasshook__": 2, "_raw_saved_result": 2, "_register_hook_dict": 2, "_saved_result": [2, 2130], "metadata": [2, 4, 6, 13, 31, 43, 44, 55, 57, 70, 78, 79, 955, 1018, 1387, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1927, 2104, 2108, 2121, 2130, 2136, 2137, 2149, 2158, 2159, 2165, 2166, 2170, 2172, 2175, 2193, 2202], "next_funct": 2, "register_prehook": [2, 2130], "allclos": [2, 64, 65, 68, 948, 949, 1086, 1167, 1168, 1174, 1175, 1203, 1204, 1206, 1207, 1208, 1209, 1213, 1214, 1363, 1365, 1376, 1378, 1381, 1382, 1407, 1795, 1822, 1895, 2048, 2098, 2104, 2137, 2156, 2170], "pack": [2, 34, 748, 749, 750, 751, 752, 753, 755, 756, 770, 791, 1331, 1332, 1407, 1518, 1533, 1552, 1598, 1816, 1817, 1818, 1819, 2098, 2110, 2121, 2130, 2138, 2150, 2160, 2191], "unpack": [2, 70, 75, 76, 770, 925, 928, 1331, 1363, 1407, 1518, 1819, 1821, 1828, 1838, 2100, 2101, 2130, 2136, 2138], "hook": [2, 29, 30, 33, 34, 36, 59, 486, 487, 758, 767, 929, 932, 934, 953, 954, 957, 1089, 1315, 1518, 1582, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1791, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1808, 1813, 1824, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 2096, 2122, 2135, 2136, 2143, 2151, 2152, 2157, 2158, 2160, 2192, 2193, 2194, 2195], "common": [2, 4, 9, 24, 35, 40, 50, 64, 70, 695, 805, 894, 974, 977, 982, 1003, 1021, 1140, 1145, 1190, 1191, 1192, 1193, 1239, 1257, 1338, 1387, 1467, 1496, 1497, 1498, 1501, 1622, 1670, 1763, 1764, 1766, 1767, 1768, 1769, 1770, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1915, 1927, 1994, 2039, 2100, 2105, 2120, 2121, 2130, 2133, 2136, 2137, 2138, 2144, 2146, 2149, 2158, 2169, 2170, 2171, 2172, 2174, 2177, 2188, 2189, 2190, 2194, 2199, 2201, 2202, 2203, 2204, 2206], "trade": [2, 6, 9, 33, 59, 1379, 1495, 1939, 2132, 2138, 2161], "leav": [2, 9, 40, 51, 57, 149, 922, 1222, 1320, 1333, 1793, 1796, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1988, 2097, 2099, 2100, 2108, 2130, 2133, 2152, 2158, 2161, 2191, 2203], "especi": [2, 10, 12, 24, 29, 51, 56, 68, 253, 894, 983, 1001, 1012, 1758, 2099, 2108, 2130, 2136, 2137, 2147, 2149, 2160, 2165, 2170, 2188, 2193, 2194, 2196, 2203], "notic": [2, 13, 29, 56, 69, 699, 833, 1165, 1179, 1223, 1410, 1420, 1494, 1635, 1705, 1865, 1866, 1872, 1873, 1879, 2032, 2096, 2097, 2125, 2130, 2170, 2185, 2191, 2194, 2197, 2199, 2201], "fit": [2, 10, 42, 63, 64, 498, 961, 1312, 1846, 1917, 2133, 2152, 2177, 2204], "evalu": [2, 5, 9, 10, 33, 34, 38, 55, 57, 62, 68, 703, 709, 811, 892, 948, 1013, 1101, 1209, 1210, 1219, 1227, 1229, 1254, 1315, 1486, 1490, 1496, 1497, 1498, 1519, 1536, 1544, 1545, 1546, 1554, 1582, 1596, 1601, 1622, 1732, 1740, 1755, 1793, 1846, 1868, 1880, 1902, 2100, 2101, 2136, 2144, 2151, 2152, 2170, 2179, 2182, 2190, 2192, 2193], "saved_tensors_hook": [2, 929, 932, 934, 2130], "pack_hook": [2, 2130], "unpack_hook": [2, 2130], "pair": [2, 29, 31, 37, 38, 50, 53, 55, 616, 745, 894, 968, 989, 1026, 1179, 1225, 1229, 1329, 1379, 1417, 1574, 1583, 1588, 1592, 1729, 1861, 1911, 2099, 2100, 2106, 2119, 2126, 2130, 2133, 2165, 2166, 2167, 2175, 2177, 2181, 2194, 2201, 2203], "retriev": [2, 24, 25, 29, 34, 35, 50, 68, 86, 536, 918, 919, 934, 935, 1056, 1227, 1246, 1524, 1528, 1634, 1679, 1772, 1817, 1837, 1866, 1971, 2121, 2130, 2143, 2155, 2158, 2165, 2166, 2167, 2184, 2192, 2206], "everytim": 2, "store": [2, 4, 6, 15, 19, 25, 31, 35, 40, 44, 51, 55, 56, 57, 59, 68, 325, 331, 397, 696, 840, 845, 919, 934, 981, 1051, 1053, 1055, 1231, 1259, 1315, 1321, 1323, 1326, 1346, 1357, 1358, 1359, 1388, 1393, 1405, 1496, 1497, 1498, 1524, 1582, 1611, 1772, 1789, 1792, 1805, 1806, 1807, 1809, 1810, 1811, 1812, 1816, 1880, 1939, 1976, 2095, 2097, 2104, 2108, 2120, 2121, 2122, 2135, 2136, 2137, 2138, 2143, 2157, 2158, 2160, 2165, 2166, 2167, 2170, 2172, 2175, 2176, 2181, 2184, 2185, 2191, 2192, 2193, 2196, 2208], "content": [2, 4, 8, 20, 31, 36, 43, 55, 56, 68, 929, 932, 934, 953, 1323, 1326, 1346, 1357, 1359, 1364, 1377, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1988, 2020, 2041, 2095, 2100, 2111, 2118, 2130, 2133, 2146, 2157, 2165, 2174, 2175, 2176, 2188, 2199, 2203, 2204], "equal": [2, 13, 20, 25, 29, 38, 50, 51, 53, 55, 68, 259, 498, 544, 616, 680, 704, 705, 706, 749, 750, 751, 752, 753, 770, 778, 779, 838, 839, 840, 845, 890, 905, 970, 977, 979, 990, 991, 1015, 1026, 1053, 1055, 1072, 1115, 1134, 1150, 1189, 1204, 1205, 1222, 1225, 1229, 1239, 1240, 1255, 1258, 1273, 1274, 1276, 1277, 1278, 1300, 1304, 1340, 1361, 1374, 1378, 1381, 1382, 1405, 1406, 1417, 1439, 1479, 1483, 1484, 1485, 1487, 1488, 1489, 1494, 1495, 1501, 1510, 1511, 1513, 1514, 1518, 1525, 1528, 1529, 1530, 1533, 1535, 1552, 1587, 1588, 1596, 1598, 1634, 1643, 1656, 1657, 1661, 1664, 1667, 1679, 1680, 1683, 1684, 1690, 1697, 1772, 1820, 1880, 1883, 1896, 1952, 1953, 1958, 1985, 1993, 2015, 2031, 2037, 2051, 2098, 2101, 2107, 2119, 2120, 2121, 2130, 2131, 2136, 2141, 2147, 2156, 2157, 2171, 2172, 2175, 2177, 2191, 2202], "term": [2, 9, 10, 38, 50, 56, 57, 68, 557, 744, 773, 804, 991, 1013, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1176, 1177, 1178, 1181, 1193, 1229, 1236, 1347, 1486, 1494, 1535, 1542, 1552, 1553, 1587, 1596, 1614, 1685, 1697, 1732, 1744, 1772, 1789, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1863, 1915, 1951, 1959, 2096, 2100, 2121, 2129, 2130, 2136, 2137, 2138, 2141, 2146, 2155, 2157, 2158, 2160, 2166, 2170, 2182, 2190, 2191, 2194, 2203], "mulbackward0": [2, 938, 941, 942, 2136], "inplac": [2, 31, 55, 62, 64, 68, 70, 78, 79, 765, 768, 786, 788, 790, 803, 810, 828, 833, 865, 867, 868, 886, 887, 892, 929, 930, 934, 955, 1001, 1203, 1315, 1490, 1500, 1519, 1520, 1521, 1522, 1523, 1526, 1538, 1539, 1540, 1568, 1581, 1582, 1601, 1602, 1603, 1610, 1612, 1625, 1654, 1662, 1673, 1674, 1675, 1676, 1677, 1681, 1692, 1693, 1694, 1703, 1719, 1734, 1735, 1738, 1741, 1743, 1753, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1851, 1852, 1859, 1860, 1861, 1862, 1863, 1880, 2036, 2098, 2107, 2130, 2136, 2155, 2181, 2198, 2202], "lead": [2, 6, 8, 23, 24, 29, 39, 56, 59, 65, 68, 894, 929, 930, 934, 937, 939, 943, 949, 1003, 1196, 1214, 1219, 1229, 1346, 1552, 1598, 1614, 1880, 1982, 1988, 2048, 2100, 2104, 2120, 2121, 2130, 2132, 2135, 2136, 2137, 2139, 2146, 2147, 2150, 2157, 2158, 2160, 2165, 2170, 2175, 2176, 2188, 2195, 2203], "undefin": [2, 29, 36, 39, 43, 318, 470, 616, 907, 929, 933, 934, 935, 948, 949, 977, 985, 1003, 1148, 1198, 1199, 1415, 1494, 1836, 1889, 2104, 2126, 2130, 2133, 2136, 2137, 2148, 2165], "recurs": [2, 33, 38, 55, 57, 59, 68, 1003, 1007, 1137, 1227, 1236, 1255, 1315, 1327, 1329, 1582, 1781, 1782, 1866, 1878, 1880, 2099, 2136, 2144, 2158, 2165, 2191, 2192, 2194, 2195, 2203], "inner": [2, 4, 59, 60, 65, 829, 831, 937, 1128, 1204, 1208, 1213, 1226, 1234, 2021, 2098, 2136, 2156, 2165, 2181, 2203], "save_on_cpu": 2, "pin_memori": [2, 24, 33, 70, 72, 75, 79, 80, 81, 444, 445, 446, 447, 448, 1146, 1148, 1197, 1904, 1908, 1910, 1979, 1980, 1981, 1982, 1983, 1984, 2014, 2097, 2098, 2104, 2121, 2133, 2136, 2156, 2172, 2198], "within": [2, 6, 10, 13, 20, 24, 25, 29, 34, 35, 36, 37, 38, 50, 51, 53, 55, 56, 59, 67, 68, 86, 87, 487, 907, 924, 948, 949, 955, 1001, 1018, 1044, 1045, 1128, 1218, 1219, 1227, 1233, 1315, 1319, 1408, 1445, 1446, 1491, 1492, 1493, 1501, 1520, 1521, 1522, 1526, 1528, 1575, 1576, 1577, 1582, 1588, 1622, 1634, 1688, 1698, 1713, 1714, 1715, 1772, 1793, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1807, 1809, 1810, 1811, 1812, 1813, 1880, 1900, 1901, 1931, 1952, 2035, 2039, 2054, 2055, 2097, 2099, 2100, 2112, 2113, 2118, 2126, 2128, 2132, 2133, 2136, 2142, 2143, 2144, 2147, 2149, 2155, 2157, 2158, 2160, 2163, 2164, 2165, 2166, 2172, 2175, 2179, 2184, 2190, 2191, 2194, 2196, 2202, 2203, 2204], "move": [2, 6, 8, 9, 10, 15, 29, 31, 55, 59, 66, 68, 85, 523, 588, 839, 1165, 1236, 1315, 1323, 1382, 1387, 1423, 1444, 1496, 1497, 1498, 1555, 1556, 1557, 1582, 1622, 1727, 1775, 1776, 1860, 1880, 2099, 2110, 2111, 2118, 2120, 2126, 2133, 2134, 2137, 2138, 2144, 2145, 2146, 2149, 2157, 2158, 2160, 2165, 2172, 2173, 2177, 2194, 2204], "copi": [2, 8, 12, 24, 25, 29, 31, 33, 40, 48, 55, 59, 63, 64, 68, 189, 195, 205, 208, 314, 401, 447, 457, 462, 470, 492, 498, 579, 580, 581, 582, 602, 616, 622, 804, 828, 865, 867, 908, 909, 927, 944, 945, 946, 968, 990, 998, 1001, 1051, 1052, 1184, 1185, 1186, 1187, 1189, 1203, 1229, 1315, 1327, 1387, 1388, 1478, 1518, 1528, 1582, 1592, 1634, 1772, 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766, 785, 787, 789, 790, 795, 796, 797, 809, 812, 834, 836, 843, 844, 846, 862, 903, 904, 905, 907, 921, 929, 932, 934, 935, 952, 957, 964, 970, 972, 979, 986, 988, 995, 1000, 1001, 1003, 1025, 1026, 1030, 1031, 1035, 1036, 1040, 1041, 1044, 1046, 1047, 1050, 1051, 1052, 1053, 1054, 1055, 1058, 1059, 1060, 1061, 1062, 1063, 1068, 1069, 1070, 1072, 1073, 1075, 1076, 1080, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1097, 1098, 1099, 1101, 1102, 1103, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1119, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1132, 1133, 1134, 1135, 1136, 1137, 1143, 1146, 1148, 1158, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1184, 1197, 1199, 1200, 1204, 1205, 1206, 1207, 1208, 1214, 1221, 1224, 1227, 1229, 1246, 1250, 1256, 1263, 1266, 1267, 1269, 1273, 1274, 1276, 1277, 1278, 1279, 1287, 1288, 1289, 1290, 1291, 1312, 1313, 1315, 1316, 1318, 1321, 1322, 1327, 1335, 1337, 1347, 1348, 1368, 1369, 1372, 1381, 1382, 1383, 1384, 1385, 1386, 1388, 1395, 1402, 1403, 1408, 1413, 1415, 1416, 1418, 1421, 1423, 1424, 1425, 1426, 1429, 1430, 1437, 1440, 1445, 1447, 1448, 1449, 1450, 1451, 1453, 1454, 1458, 1459, 1460, 1461, 1462, 1468, 1473, 1474, 1475, 1476, 1477, 1478, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1491, 1492, 1493, 1496, 1497, 1498, 1499, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1516, 1517, 1518, 1524, 1525, 1527, 1528, 1529, 1530, 1532, 1534, 1536, 1544, 1545, 1546, 1549, 1550, 1551, 1553, 1554, 1558, 1559, 1560, 1561, 1562, 1563, 1567, 1569, 1570, 1572, 1575, 1576, 1577, 1578, 1579, 1580, 1582, 1584, 1587, 1589, 1590, 1593, 1594, 1595, 1597, 1600, 1604, 1605, 1606, 1607, 1608, 1609, 1616, 1618, 1622, 1626, 1627, 1628, 1629, 1630, 1631, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1670, 1671, 1672, 1679, 1680, 1687, 1690, 1699, 1707, 1724, 1725, 1726, 1730, 1731, 1746, 1747, 1755, 1759, 1760, 1761, 1771, 1772, 1790, 1791, 1800, 1801, 1803, 1804, 1806, 1809, 1810, 1811, 1812, 1816, 1817, 1819, 1824, 1827, 1830, 1831, 1833, 1834, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1865, 1866, 1867, 1868, 1869, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1885, 1886, 1893, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1904, 1906, 1907, 1908, 1910, 1916, 1917, 1918, 1921, 1922, 1923, 1924, 1927, 1932, 1933, 1934, 1938, 1941, 1942, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1964, 1968, 1972, 1975, 1976, 1978, 1985, 1988, 1990, 1991, 1992, 1993, 1996, 1998, 2003, 2005, 2011, 2015, 2016, 2018, 2020, 2021, 2024, 2025, 2026, 2027, 2030, 2031, 2032, 2033, 2034, 2035, 2038, 2039, 2042, 2043, 2044, 2048, 2049, 2054, 2056, 2057, 2058, 2059, 2063, 2064, 2065, 2067, 2069, 2071, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2083, 2084, 2087, 2088, 2092, 2093, 2097, 2098, 2099, 2100, 2101, 2104, 2113, 2118, 2119, 2121, 2126, 2128, 2133, 2137, 2146, 2149, 2152, 2155, 2156, 2159, 2160, 2164, 2165, 2169, 2171, 2172, 2173, 2175, 2176, 2177, 2179, 2184, 2190, 2194, 2198, 2202, 2203, 2206, 2209], "show": [3, 5, 8, 14, 19, 24, 25, 29, 31, 35, 39, 55, 57, 59, 68, 937, 943, 1001, 1196, 1315, 1417, 1582, 1762, 1837, 1880, 2095, 2100, 2119, 2121, 2132, 2133, 2135, 2136, 2141, 2142, 2144, 2151, 2152, 2155, 2157, 2158, 2159, 2165, 2167, 2172, 2188, 2191, 2196, 2197, 2200, 2201, 2204, 2206], "max_siz": [3, 49, 51, 2133], "capac": [3, 1115, 2133, 2146], "preferred_blas_librari": 3, "overrid": [3, 6, 15, 20, 21, 25, 29, 30, 31, 33, 36, 38, 43, 51, 55, 57, 59, 64, 68, 804, 805, 883, 919, 921, 934, 935, 1086, 1315, 1494, 1495, 1515, 1517, 1541, 1548, 1573, 1574, 1582, 1585, 1586, 1587, 1589, 1596, 1599, 1614, 1615, 1631, 1660, 1661, 1671, 1700, 1724, 1732, 1797, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 1880, 1927, 1943, 2096, 2100, 2104, 2108, 2149, 2152, 2157, 2158, 2160, 2165, 2170, 2175, 2181, 2202, 2204], "bla": [3, 20, 2132], "choos": [3, 10, 16, 20, 68, 921, 934, 935, 961, 1013, 1066, 1361, 1374, 1379, 1494, 1998, 2128, 2129, 2132, 2152, 2158, 2161, 2175, 2184], "cubla": [3, 12, 18, 20, 21, 1057, 2036, 2148, 2188], "cublaslt": [3, 18, 21], "ck": 3, "rocm": [3, 18, 696, 699, 969, 981, 1085, 1410, 1420, 1509, 1510, 1511, 1512, 1513, 1514, 1534, 1553, 1569, 2096], "subject": [3, 4, 12, 19, 20, 24, 29, 31, 34, 36, 37, 59, 67, 68, 69, 232, 894, 1641, 1642, 1643, 1646, 1740, 1772, 2100, 2119, 2120, 2125, 2130, 2136, 2149, 2155, 2159, 2160, 2161, 2165, 2170, 2172, 2173, 2179, 2180, 2181, 2184, 2190, 2200], "hipbla": [3, 18, 20], "hipblaslt": [3, 18, 20], "offer": [3, 29, 31, 33, 36, 59, 62, 1588, 1626, 1627, 1628, 1629, 1630, 1772, 2036, 2133, 2136, 2142, 2153, 2158, 2159, 2165, 2170, 2189, 2194, 2198, 2207], "wherev": [3, 10, 2119], "prefer": [3, 6, 10, 24, 29, 31, 40, 51, 59, 891, 907, 939, 1219, 1242, 1313, 1356, 1369, 1373, 1381, 1388, 1589, 1740, 1931, 1993, 2014, 2097, 2104, 2108, 2130, 2133, 2147, 2155, 2157, 2158, 2170, 2194, 2203], "environ": [3, 4, 8, 15, 16, 19, 20, 23, 25, 31, 35, 36, 38, 40, 43, 48, 54, 55, 57, 68, 680, 1056, 1227, 1229, 1252, 1325, 1331, 1552, 1598, 2036, 2095, 2096, 2097, 2106, 2130, 2132, 2133, 2135, 2142, 2147, 2148, 2150, 2165, 2183, 2184, 2185, 2187, 2189, 2194, 2203, 2204], "variabl": [3, 4, 6, 13, 15, 19, 20, 23, 25, 33, 35, 36, 38, 40, 43, 48, 50, 54, 56, 57, 59, 64, 68, 70, 73, 78, 79, 447, 680, 722, 723, 724, 725, 726, 727, 728, 729, 730, 733, 734, 735, 736, 738, 739, 740, 741, 742, 744, 748, 749, 750, 751, 752, 753, 755, 756, 766, 770, 774, 813, 843, 844, 846, 929, 930, 934, 1003, 1018, 1022, 1026, 1114, 1146, 1214, 1229, 1329, 1331, 1388, 1499, 1509, 1510, 1511, 1512, 1513, 1514, 1518, 1524, 1525, 1533, 1534, 1552, 1553, 1554, 1567, 1569, 1582, 1585, 1590, 1598, 1600, 1772, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1834, 1838, 1840, 1864, 1865, 1866, 1867, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1878, 1879, 1881, 1904, 1906, 1908, 2036, 2048, 2093, 2095, 2096, 2106, 2107, 2120, 2121, 2130, 2132, 2133, 2135, 2138, 2141, 2142, 2147, 2148, 2150, 2152, 2155, 2157, 2160, 2165, 2172, 2175, 2183, 2184, 2185, 2190, 2191, 2192, 2194, 2196, 2203, 2204], "torch_blas_prefer_cublaslt": 3, "global": [3, 4, 6, 9, 12, 13, 21, 24, 29, 31, 34, 36, 37, 38, 40, 51, 59, 64, 67, 68, 70, 73, 78, 79, 883, 890, 894, 902, 957, 970, 979, 1018, 1037, 1063, 1077, 1095, 1103, 1146, 1148, 1158, 1163, 1181, 1197, 1200, 1203, 1273, 1274, 1296, 1303, 1315, 1318, 1331, 1335, 1386, 1402, 1582, 1740, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1802, 1806, 1834, 1880, 1904, 1906, 1908, 1911, 1935, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2078, 2093, 2097, 2100, 2101, 2104, 2112, 2122, 2129, 2136, 2143, 2144, 2146, 2148, 2160, 2165, 2166, 2167, 2170, 2173, 2175, 2191, 2192, 2193, 2194, 2195, 2202, 2204], "overridden": [3, 6, 15, 68, 918, 919, 920, 934, 935, 1086, 1582, 1971, 2100, 2130, 2136, 2147, 2204, 2205], "achiev": [3, 20, 24, 25, 29, 31, 37, 38, 51, 59, 1145, 1210, 1315, 1495, 1501, 1582, 1588, 1589, 1661, 1690, 1772, 1880, 2114, 2133, 2143, 2146, 2158, 2165, 2167, 2172, 2204], "better": [3, 4, 8, 9, 10, 15, 24, 28, 29, 40, 55, 63, 944, 1001, 1114, 1202, 1206, 1207, 1223, 1302, 1331, 1517, 1616, 1740, 1746, 1772, 1781, 1782, 1875, 1880, 1902, 2100, 2121, 2129, 2130, 2132, 2133, 2139, 2141, 2148, 2150, 2155, 2157, 2159, 2160, 2161, 2170, 2175, 2181, 2184, 2189, 2191, 2192, 2200, 2203], "select": [3, 6, 16, 18, 20, 22, 24, 29, 31, 38, 40, 48, 56, 312, 314, 316, 320, 684, 685, 686, 770, 991, 1028, 1030, 1034, 1045, 1046, 1050, 1058, 1059, 1060, 1061, 1063, 1064, 1072, 1088, 1092, 1094, 1095, 1097, 1099, 1101, 1102, 1103, 1108, 1109, 1110, 1111, 1114, 1115, 1118, 1120, 1122, 1123, 1165, 1388, 1417, 1446, 1447, 1448, 1449, 1450, 1461, 1463, 1465, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1533, 1552, 1598, 1646, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1811, 1812, 1934, 2011, 2039, 2051, 2055, 2056, 2057, 2058, 2060, 2076, 2077, 2078, 2079, 2080, 2081, 2083, 2084, 2087, 2090, 2091, 2097, 2098, 2100, 2118, 2119, 2120, 2121, 2130, 2132, 2133, 2141, 2148, 2151, 2156, 2160, 2170, 2174, 2177, 2192, 2198, 2200, 2203, 2206], "incorrect": [3, 5, 6, 59, 68, 253, 512, 555, 929, 932, 934, 948, 957, 983, 1003, 1013, 1331, 1374, 1387, 1588, 1626, 1627, 1628, 1629, 1630, 1758, 1830, 1982, 1993, 2097, 2100, 2104, 2133, 2147, 2155, 2191, 2207], "_blasbackend": 3, "preferred_rocm_fa_librari": 3, "flash": [3, 1641, 1646], "attent": [3, 8, 36, 37, 745, 1495, 1588, 1626, 1628, 1630, 1641, 1643, 1661, 1740, 2096, 2150, 2174, 2191], "aotriton": 3, "experimeent": 3, "composable_kernel": 3, "torch_rocm_fa_prefer_ck": 3, "_rocmfabackend": 3, "preferred_linalg_librari": [3, 1352], "heurist": [3, 15, 24, 33, 50, 51, 68, 1006, 1145, 2139, 2188, 2197], "cusolv": [3, 1379, 1380, 1997], "magma": [3, 1361, 1405, 1895, 1997, 2150, 2170], "algebra": [3, 10, 1145, 1352, 1373, 2105], "decid": [3, 5, 8, 29, 40, 50, 57, 63, 485, 1225, 1806, 2107, 2155, 2170, 2193, 2194, 2197], "pick": [3, 29, 49, 51, 55, 512, 1001, 2130, 2165, 2190, 2192, 2197], "torch_linalg_prefer_cusolv": 3, "linalg": [3, 12, 405, 991, 992, 993, 994, 1036, 1131, 1259, 1293, 1396, 1405, 1406, 1407, 1411, 1412, 1789, 1790, 1795, 1830, 1882, 1887, 1889, 1895, 1965, 1977, 1997, 1998, 2023, 2045, 2096], "inv": [3, 38, 1293, 1347, 1351, 1357, 1373, 1377, 1381], "inv_ex": [3, 1356], "cholesky_ex": [3, 1345], "lu_factor": [3, 1364, 1365, 1405, 1406, 1407], "lu": [3, 11, 1357, 1363, 1364, 1365, 1406, 1407, 2098], "eigh": [3, 1345, 1351, 1354, 1373, 1379, 2147], "eighval": 3, "svdval": [3, 1347, 1361, 1370, 1379, 1997, 2147], "_linalgbackend": 3, "sdpaparam": 3, "flash_sdp_en": 3, "scale": [3, 8, 24, 35, 38, 40, 49, 51, 96, 172, 472, 474, 616, 695, 699, 700, 701, 748, 749, 750, 751, 752, 753, 754, 759, 760, 761, 762, 763, 764, 765, 766, 769, 780, 782, 783, 784, 785, 787, 790, 791, 807, 813, 816, 834, 836, 837, 838, 839, 840, 845, 853, 969, 1159, 1160, 1163, 1181, 1213, 1214, 1402, 1490, 1519, 1524, 1525, 1526, 1542, 1554, 1610, 1635, 1636, 1637, 1641, 1646, 1679, 1680, 1681, 1697, 1699, 1740, 1741, 1746, 1780, 1840, 1841, 1868, 1897, 1898, 1899, 1900, 1901, 1974, 1975, 1994, 2048, 2096, 2098, 2120, 2121, 2126, 2128, 2133, 2138, 2140, 2144, 2160, 2161, 2163, 2171, 2194], "product": [3, 16, 29, 31, 36, 38, 50, 149, 696, 699, 700, 701, 770, 922, 927, 938, 940, 941, 942, 943, 969, 981, 987, 991, 1022, 1036, 1050, 1103, 1108, 1122, 1123, 1126, 1142, 1145, 1209, 1213, 1214, 1292, 1336, 1348, 1355, 1381, 1382, 1384, 1410, 1417, 1420, 1470, 1533, 1534, 1552, 1553, 1641, 1646, 1687, 1740, 1789, 1883, 1884, 1893, 1939, 2016, 2031, 2045, 2048, 2097, 2119, 2121, 2126, 2130, 2141, 2143, 2158, 2160, 2170, 2191, 2203, 2206], "enable_mem_efficient_sdp": [3, 1740], "mem_efficient_sdp_en": 3, "enable_flash_sdp": [3, 1740], "math_sdp_en": 3, "math": [3, 24, 68, 1155, 1367, 1641, 1646, 1700, 1740, 1885, 1902, 1998, 2097, 2099, 2100, 2115, 2139, 2147, 2170, 2171, 2177, 2185, 2204], "enable_math_sdp": [3, 1740], "fp16_bf16_reduction_math_sdp_allow": 3, "allow_fp16_bf16_reduction_math_sdp": [3, 2147], "cudnn_sdp_en": 3, "enable_cudnn_sdp": 3, "is_flash_attention_avail": 3, "check": [3, 4, 5, 6, 12, 15, 20, 21, 24, 29, 30, 31, 36, 38, 50, 59, 70, 71, 76, 79, 85, 87, 220, 232, 339, 498, 683, 688, 704, 890, 929, 930, 932, 933, 934, 935, 948, 949, 955, 1003, 1014, 1039, 1040, 1044, 1048, 1081, 1101, 1225, 1229, 1237, 1238, 1248, 1249, 1250, 1302, 1312, 1315, 1331, 1332, 1345, 1346, 1351, 1352, 1354, 1357, 1359, 1364, 1370, 1373, 1377, 1379, 1380, 1405, 1432, 1444, 1445, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1582, 1762, 1772, 1792, 1795, 1808, 1818, 1837, 1838, 1880, 1971, 1979, 1980, 1981, 1982, 1983, 1984, 2037, 2053, 2054, 2081, 2095, 2099, 2100, 2101, 2104, 2110, 2119, 2120, 2129, 2133, 2136, 2137, 2138, 2139, 2143, 2144, 2145, 2148, 2149, 2152, 2155, 2157, 2158, 2159, 2160, 2165, 2170, 2172, 2175, 2177, 2187, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2202, 2203, 2204, 2205, 2208], "flashattent": [3, 1630, 1740], "depend": [3, 5, 6, 13, 15, 24, 29, 31, 33, 34, 35, 36, 38, 40, 50, 51, 53, 56, 59, 68, 85, 314, 795, 907, 953, 1050, 1103, 1108, 1122, 1123, 1166, 1168, 1176, 1177, 1178, 1204, 1208, 1213, 1229, 1239, 1241, 1242, 1243, 1246, 1327, 1331, 1351, 1352, 1379, 1405, 1410, 1444, 1494, 1495, 1515, 1517, 1525, 1528, 1541, 1547, 1548, 1573, 1574, 1585, 1586, 1587, 1589, 1596, 1614, 1615, 1631, 1634, 1635, 1653, 1660, 1661, 1671, 1680, 1688, 1700, 1724, 1732, 1740, 1759, 1762, 1772, 1795, 1796, 1802, 1868, 1883, 1885, 1902, 1918, 1979, 1980, 1981, 1983, 1984, 1997, 2023, 2051, 2095, 2097, 2099, 2100, 2104, 2107, 2108, 2118, 2126, 2130, 2132, 2133, 2136, 2137, 2139, 2141, 2144, 2146, 2151, 2154, 2155, 2159, 2160, 2165, 2170, 2176, 2177, 2182, 2184, 2188, 2189, 2190, 2191, 2193, 2194, 2196, 2199, 2201, 2202, 2206], "can_use_flash_attent": 3, "debug": [3, 5, 6, 9, 20, 21, 24, 35, 41, 47, 55, 680, 812, 833, 851, 854, 858, 872, 948, 949, 1001, 1003, 1011, 1037, 1076, 1119, 1263, 1345, 1765, 1766, 1767, 1768, 1938, 1945, 2099, 2106, 2130, 2132, 2133, 2135, 2142, 2143, 2144, 2148, 2150, 2155, 2158, 2180, 2190, 2191, 2192, 2195, 2196, 2199, 2206, 2207, 2208], "util": [3, 8, 18, 31, 51, 55, 56, 57, 59, 60, 67, 68, 70, 498, 742, 745, 748, 749, 750, 755, 756, 766, 770, 774, 802, 811, 935, 1055, 1146, 1147, 1148, 1196, 1238, 1248, 1249, 1250, 1253, 1315, 1533, 1552, 1582, 1598, 1599, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1792, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1816, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1868, 1875, 1880, 2001, 2003, 2005, 2006, 2007, 2036, 2096, 2100, 2104, 2108, 2113, 2121, 2129, 2131, 2132, 2133, 2136, 2138, 2140, 2141, 2143, 2144, 2146, 2148, 2153, 2155, 2158, 2184, 2190, 2191, 2194, 2201, 2204, 2207], "_sdpaparam": 3, "kei": [3, 4, 24, 31, 35, 40, 48, 50, 51, 55, 56, 57, 59, 66, 68, 414, 602, 680, 745, 812, 883, 888, 956, 961, 968, 1111, 1202, 1212, 1230, 1315, 1331, 1332, 1387, 1582, 1583, 1588, 1592, 1626, 1627, 1628, 1629, 1630, 1643, 1740, 1809, 1825, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 1880, 2037, 2039, 2083, 2084, 2098, 2099, 2100, 2104, 2112, 2126, 2133, 2136, 2143, 2144, 2149, 2155, 2156, 2157, 2159, 2160, 2163, 2165, 2166, 2172, 2175, 2177, 2180, 2182, 2195, 2200, 2201, 2202, 2207], "mask": [3, 48, 398, 399, 400, 401, 402, 543, 745, 948, 949, 1409, 1490, 1501, 1526, 1588, 1626, 1627, 1628, 1629, 1630, 1681, 1740, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1809, 1810, 1811, 1812, 1863, 2096, 2098, 2104, 2119, 2120, 2121, 2126, 2170, 2194, 2198], "dropout": [3, 6, 37, 68, 744, 745, 770, 1315, 1490, 1520, 1521, 1522, 1526, 1533, 1552, 1582, 1588, 1598, 1599, 1626, 1628, 1630, 1654, 1674, 1675, 1676, 1681, 1740, 1880, 2098, 2110, 2120, 2130, 2133, 2156, 2162], "causal": [3, 745, 1588, 1626, 1627, 1628, 1629, 1630, 1642, 1643, 1644, 1645, 1740, 2126], "warn": [3, 4, 15, 19, 20, 25, 26, 29, 34, 35, 36, 55, 680, 937, 943, 991, 1050, 1103, 1108, 1119, 1122, 1123, 1214, 1229, 1296, 1312, 1331, 1373, 1387, 1417, 1588, 1626, 1627, 1628, 1629, 1630, 1740, 1772, 1792, 1795, 1938, 1945, 1993, 2036, 2048, 2095, 2106, 2113, 2126, 2131, 2136, 2149, 2152, 2156, 2158, 2164, 2191, 2203, 2204], "why": [3, 4, 8, 24, 55, 64, 68, 1145, 1319, 1740, 1843, 1844, 1862, 2104, 2107, 2137, 2139, 2190, 2191, 2193, 2201, 2206], "could": [3, 5, 6, 8, 9, 18, 20, 24, 29, 35, 36, 37, 38, 40, 50, 57, 64, 67, 68, 582, 583, 584, 586, 587, 812, 957, 1081, 1166, 1168, 1176, 1177, 1178, 1230, 1312, 1315, 1321, 1323, 1346, 1387, 1405, 1434, 1435, 1582, 1781, 1782, 1867, 1880, 1973, 1998, 2032, 2099, 2100, 2107, 2118, 2130, 2133, 2135, 2149, 2150, 2153, 2155, 2158, 2160, 2161, 2165, 2166, 2167, 2170, 2174, 2175, 2189, 2191, 2194, 2195, 2199, 2203, 2204, 2207, 2208], "can_use_efficient_attent": 3, "efficient_attent": [3, 1641, 1646], "can_use_cudnn_attent": 3, "cudnn_attent": [3, 1641], "sdp_kernel": 3, "enable_flash": 3, "enable_math": 3, "enable_mem_effici": 3, "enable_cudnn": 3, "temporarili": [3, 40, 1935, 2104, 2130, 2155, 2159, 2197], "previou": [3, 19, 29, 31, 33, 50, 55, 57, 59, 68, 512, 555, 770, 1001, 1003, 1042, 1077, 1190, 1269, 1326, 1386, 1402, 1533, 1552, 1598, 1646, 1772, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1837, 1877, 1902, 1993, 2110, 2130, 2133, 2136, 2144, 2150, 2152, 2153, 2157, 2160, 2167, 2194, 2203], "restor": [3, 6, 31, 34, 36, 68, 86, 1012, 1190, 1386, 1402, 1646, 1827, 1922, 1943, 2144, 2149, 2192], "is_avail": [3, 18, 29, 683, 692, 2039, 2133, 2140, 2142, 2145, 2149, 2159, 2184, 2211], "determinist": [3, 4, 6, 23, 29, 33, 38, 68, 86, 485, 498, 512, 902, 1146, 1147, 1148, 1263, 1296, 1318, 1331, 1332, 1413, 1416, 1418, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1552, 1598, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1932, 1938, 2036, 2096, 2098, 2130, 2139, 2148, 2157, 2164], "algorithm": [3, 4, 8, 12, 20, 22, 25, 30, 34, 38, 40, 59, 86, 680, 770, 789, 795, 991, 1312, 1356, 1363, 1369, 1373, 1379, 1388, 1405, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1531, 1533, 1552, 1598, 1634, 1635, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1688, 1699, 1740, 1759, 1772, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1859, 1860, 1861, 1863, 1885, 1924, 1939, 1997, 1998, 2036, 2096, 2130, 2133, 2147, 2165, 2170], "are_deterministic_algorithms_en": [3, 2188], "use_deterministic_algorithm": [3, 28, 498, 902, 1146, 1147, 1148, 1296, 1938, 2148], "benchmark": [3, 20, 680, 2096, 2098, 2133, 2142, 2159, 2188, 2194, 2200, 2203], "fastest": [3, 18, 20, 906, 1772, 1829, 1843, 1844, 1862, 2141, 2148, 2157], "benchmark_limit": 3, "maximum": [3, 28, 38, 50, 51, 55, 498, 705, 707, 781, 807, 838, 839, 840, 845, 850, 903, 1052, 1092, 1094, 1101, 1109, 1110, 1124, 1125, 1146, 1147, 1148, 1191, 1208, 1276, 1277, 1278, 1388, 1413, 1439, 1540, 1679, 1680, 1779, 1841, 1866, 1875, 1902, 1916, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2038, 2076, 2077, 2081, 2098, 2106, 2115, 2121, 2128, 2129, 2133, 2146, 2156, 2163, 2177, 2178, 2190, 2198, 2206, 2208], "try": [3, 4, 5, 8, 9, 29, 31, 36, 37, 42, 43, 47, 50, 55, 63, 64, 232, 1001, 1003, 1115, 1199, 1208, 1214, 1229, 1254, 1320, 1322, 1331, 1352, 1439, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1626, 1627, 1629, 1635, 1641, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1772, 1839, 1840, 1842, 1843, 1844, 1845, 1846, 1847, 1859, 1860, 1861, 1862, 2048, 2095, 2097, 2100, 2101, 2104, 2130, 2133, 2136, 2138, 2141, 2146, 2155, 2157, 2158, 2160, 2161, 2165, 2170, 2189, 2190, 2193, 2194, 2196, 2203, 2204, 2206], "dispatch": [3, 15, 29, 55, 59, 68, 1215, 1216, 1230, 1320, 1434, 1435, 2037, 2039, 2100, 2104, 2133, 2136, 2152, 2155, 2194, 2195, 2205], "v8": [3, 21], "api": [3, 4, 6, 9, 10, 15, 21, 25, 29, 30, 31, 33, 37, 40, 42, 43, 47, 48, 49, 50, 51, 53, 59, 60, 63, 67, 69, 70, 75, 79, 80, 81, 232, 814, 842, 890, 924, 925, 926, 927, 928, 940, 945, 946, 990, 1003, 1006, 1013, 1037, 1056, 1077, 1078, 1079, 1086, 1087, 1089, 1118, 1149, 1189, 1203, 1206, 1207, 1209, 1214, 1227, 1239, 1326, 1327, 1329, 1439, 1463, 1772, 1825, 1827, 1828, 1836, 1924, 1935, 2029, 2039, 2048, 2070, 2090, 2095, 2106, 2107, 2108, 2114, 2115, 2118, 2119, 2121, 2125, 2130, 2135, 2140, 2149, 2151, 2161, 2165, 2166, 2170, 2173, 2175, 2179, 2182, 2184, 2185, 2192, 2196, 2203, 2205, 2208], "get_fastpath_en": 3, "fast": [3, 8, 9, 24, 29, 40, 68, 948, 1523, 1544, 1545, 1546, 1614, 1630, 1781, 1782, 1875, 1883, 1902, 1924, 1939, 2115, 2132, 2133, 2136, 2142, 2157, 2165, 2170, 2173, 2174, 2182, 2191, 2193, 2196, 2197, 2206], "path": [3, 4, 5, 9, 15, 21, 29, 31, 40, 48, 50, 53, 54, 55, 57, 59, 68, 744, 958, 959, 960, 1037, 1145, 1238, 1315, 1582, 1599, 1630, 1837, 1880, 2095, 2098, 2099, 2130, 2133, 2136, 2139, 2142, 2149, 2152, 2155, 2157, 2158, 2159, 2170, 2180, 2184, 2188, 2196, 2197], "transformerencod": 3, "multiheadattent": [3, 1626, 1628, 1630, 2160], "fastpath": [3, 1588, 2193], "condit": [3, 6, 13, 22, 24, 55, 56, 57, 64, 68, 70, 78, 79, 616, 619, 624, 704, 770, 938, 948, 949, 1013, 1018, 1312, 1331, 1345, 1347, 1352, 1355, 1361, 1362, 1363, 1374, 1379, 1533, 1552, 1598, 1630, 1818, 1823, 2023, 2051, 2097, 2098, 2099, 2101, 2109, 2130, 2136, 2147, 2175, 2190, 2191, 2192, 2193, 2198, 2203], "met": [3, 13, 616, 1018, 1355, 1362, 1363, 1374, 1388, 1630, 1982], "set_fastpath_en": 3, "verbos": [3, 15, 20, 29, 68, 680, 1229, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 2095, 2106, 2115, 2120, 2155, 2175, 2204], "On": [3, 15, 23, 24, 29, 30, 33, 38, 51, 57, 59, 64, 68, 696, 699, 969, 981, 1042, 1096, 1363, 1410, 1420, 1509, 1510, 1511, 1512, 1513, 1514, 1534, 1549, 1550, 1551, 1552, 1553, 1569, 1592, 1598, 1611, 1772, 1843, 1844, 1859, 1862, 2097, 2107, 2130, 2132, 2133, 2136, 2147, 2158, 2165, 2166, 2167, 2170, 2188, 2190, 2191, 2193, 2194, 2203], "demand": [3, 24, 1079, 2099, 2121, 2143, 2146, 2165], "onemkl": 3, "easier": [3, 8, 24, 36, 55, 68, 2097, 2099, 2126, 2130, 2131, 2136, 2137, 2139, 2170, 2194, 2203, 2204], "dump": [3, 68, 1037, 2135, 2150, 2155, 2194, 2204, 2208], "durat": [3, 20, 29, 47, 50, 966, 1434, 1435, 2113, 2152, 2159, 2194], "kernel": [3, 4, 5, 12, 15, 19, 21, 29, 59, 67, 87, 485, 680, 692, 749, 750, 751, 752, 753, 782, 783, 784, 955, 1001, 1035, 1040, 1044, 1045, 1086, 1087, 1121, 1123, 1441, 1445, 1446, 1491, 1492, 1493, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1529, 1530, 1549, 1550, 1551, 1558, 1559, 1560, 1561, 1562, 1563, 1575, 1576, 1577, 1578, 1579, 1580, 1634, 1663, 1664, 1665, 1666, 1667, 1668, 1683, 1684, 1740, 1781, 1782, 1793, 2037, 2039, 2054, 2055, 2092, 2097, 2104, 2110, 2115, 2121, 2126, 2133, 2136, 2137, 2139, 2145, 2147, 2155, 2157, 2159, 2161, 2170, 2185, 2188, 2191, 2192, 2193, 2194, 2195, 2196, 2203, 2204, 2207], "mkl_verbos": 3, "methodologi": 3, "larg": [3, 4, 8, 9, 24, 29, 35, 37, 68, 680, 887, 1024, 1026, 1044, 1101, 1352, 1361, 1379, 1486, 1528, 1634, 1682, 1772, 1875, 1963, 1976, 1998, 2054, 2081, 2096, 2118, 2120, 2121, 2132, 2133, 2136, 2138, 2144, 2147, 2149, 2152, 2155, 2158, 2161, 2165, 2170, 2173, 2176, 2188, 2194, 2201, 2203, 2204, 2206], "moreov": [3, 31, 57, 512, 1772, 1862, 2207], "investig": [3, 8, 29, 64, 2201, 2203], "singl": [3, 4, 6, 15, 20, 25, 29, 31, 34, 35, 36, 37, 38, 40, 43, 48, 49, 50, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 78, 79, 253, 680, 705, 706, 776, 777, 778, 779, 782, 783, 784, 828, 924, 937, 938, 939, 940, 941, 942, 943, 955, 970, 979, 983, 1001, 1003, 1022, 1026, 1086, 1089, 1202, 1204, 1205, 1208, 1210, 1214, 1273, 1274, 1278, 1300, 1314, 1315, 1331, 1332, 1335, 1393, 1417, 1434, 1435, 1484, 1485, 1488, 1489, 1492, 1493, 1495, 1509, 1510, 1511, 1513, 1514, 1517, 1518, 1529, 1530, 1536, 1549, 1550, 1551, 1554, 1576, 1577, 1582, 1590, 1594, 1595, 1597, 1611, 1622, 1647, 1648, 1649, 1650, 1651, 1652, 1655, 1656, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1683, 1684, 1685, 1713, 1714, 1715, 1758, 1764, 1766, 1769, 1770, 1771, 1772, 1778, 1779, 1780, 1783, 1785, 1787, 1788, 1825, 1826, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1857, 1859, 1860, 1861, 1862, 1863, 1868, 1880, 1892, 1918, 1939, 1985, 1991, 1992, 2032, 2033, 2036, 2043, 2044, 2048, 2097, 2099, 2100, 2107, 2110, 2118, 2120, 2121, 2122, 2126, 2130, 2132, 2133, 2136, 2137, 2139, 2141, 2144, 2146, 2147, 2148, 2149, 2155, 2157, 2158, 2159, 2160, 2166, 2167, 2170, 2172, 2173, 2176, 2177, 2181, 2187, 2188, 2190, 2192, 2193, 2194, 2196, 2202, 2203, 2204], "enough": [3, 9, 25, 36, 57, 68, 1229, 1236, 1327, 1632, 1979, 1980, 1981, 1982, 1983, 1984, 2036, 2099, 2118, 2121, 2133, 2136, 2141, 2157, 2158, 2173, 2191, 2194, 2203, 2205], "scope": [3, 8, 13, 53, 68, 833, 1018, 1105, 1199, 1622, 1806, 2097, 2099, 2100, 2133, 2138, 2155, 2158, 2167, 2193, 2202], "second": [3, 6, 15, 19, 29, 34, 35, 40, 43, 50, 53, 55, 65, 68, 696, 699, 701, 704, 770, 806, 903, 904, 905, 913, 921, 934, 935, 948, 949, 969, 973, 974, 976, 977, 978, 981, 1036, 1050, 1103, 1108, 1122, 1123, 1133, 1135, 1136, 1142, 1150, 1191, 1192, 1204, 1207, 1208, 1209, 1213, 1214, 1242, 1257, 1258, 1269, 1272, 1280, 1281, 1282, 1292, 1304, 1338, 1340, 1348, 1384, 1387, 1393, 1394, 1404, 1410, 1414, 1416, 1417, 1419, 1420, 1474, 1479, 1482, 1486, 1492, 1493, 1499, 1510, 1511, 1513, 1514, 1524, 1525, 1532, 1533, 1547, 1550, 1551, 1552, 1570, 1574, 1576, 1577, 1598, 1611, 1670, 1672, 1706, 1762, 1806, 1841, 1843, 1844, 1847, 1859, 1863, 1875, 1877, 1880, 1923, 1973, 1982, 2020, 2025, 2027, 2042, 2045, 2048, 2099, 2100, 2104, 2109, 2118, 2130, 2133, 2136, 2137, 2138, 2139, 2141, 2144, 2148, 2152, 2155, 2157, 2159, 2165, 2171, 2175, 2184, 2188, 2191, 2203], "verbose_on": 3, "level": [3, 4, 8, 9, 10, 13, 16, 19, 24, 26, 29, 31, 33, 35, 36, 40, 42, 43, 47, 55, 56, 57, 59, 63, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 498, 680, 757, 924, 925, 926, 927, 928, 940, 1203, 1223, 1239, 1242, 1259, 1495, 1518, 1772, 1877, 2041, 2097, 2100, 2101, 2106, 2113, 2115, 2118, 2128, 2130, 2132, 2136, 2139, 2141, 2152, 2158, 2159, 2160, 2161, 2165, 2170, 2172, 2175, 2180, 2181, 2182, 2183, 2185, 2190, 2191, 2192, 2194, 2195, 2198, 2201, 2202, 2203, 2204, 2205], "verbose_off": 3, "dnn": [3, 2132], "onednn": [3, 884, 885, 888, 1317, 1324, 2156, 2160], "former": [3, 57, 59, 1552, 1582, 2130, 2152], "dnnl_verbos": 3, "verbose_on_cr": 3, "set_flag": 3, "_enabl": 3, "opt": [3, 29, 34, 1145, 1327, 2097, 2141, 2203], "einsum": [3, 2098, 2156], "automat": [3, 15, 20, 25, 29, 31, 50, 51, 55, 68, 588, 918, 920, 934, 935, 955, 1001, 1044, 1079, 1089, 1145, 1278, 1325, 1331, 1417, 1629, 1740, 1773, 1774, 1914, 1943, 1996, 2037, 2054, 2095, 2096, 2099, 2100, 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1396, 1495, 2069, 2100, 2136, 2149, 2157, 2165, 2166, 2190, 2203], "workload": [4, 9, 20, 24, 29, 59, 1089, 2133, 2139, 2143, 2147, 2159, 2165, 2183, 2187, 2201], "good": [4, 8, 9, 15, 68, 1001, 1206, 1236, 1590, 1877, 1959, 2095, 2104, 2118, 2133, 2136, 2139, 2143, 2144, 2146, 2155, 2158, 2160, 2190, 2191, 2192, 2193, 2200, 2203], "intrins": [4, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 804, 805, 2161, 2162], "contrast": [4, 38, 40, 55, 57, 770, 1225, 1533, 1841, 1862, 2128, 2130, 2137, 2139, 2170, 2198], "adaptive_autorang": 4, "threshold": [4, 25, 68, 1370, 1373, 1542, 1614, 1619, 1697, 1748, 1754, 1841, 1877, 1943, 2098, 2129, 2133, 2156, 2175], "min_run_tim": 4, "01": [4, 30, 34, 53, 487, 765, 790, 839, 840, 1146, 1188, 1568, 1703, 1704, 1762, 1837, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1868, 1875, 1897, 1950, 1952, 1953, 1958, 1959, 2098, 2128, 2143, 2155, 2157, 2198, 2203], "max_run_tim": 4, "callback": [4, 25, 29, 40, 53, 67, 68, 1772, 2113, 2136, 2143, 2159, 2165], "similar": [4, 8, 10, 12, 21, 24, 29, 33, 35, 36, 38, 47, 56, 59, 64, 67, 68, 492, 514, 722, 723, 724, 725, 726, 727, 730, 740, 741, 742, 743, 755, 756, 766, 774, 816, 906, 921, 934, 935, 982, 987, 1000, 1001, 1191, 1192, 1315, 1515, 1516, 1528, 1541, 1544, 1545, 1546, 1582, 1631, 1670, 1816, 1830, 1831, 1841, 1863, 1880, 1889, 1917, 1924, 1973, 1977, 2011, 2017, 2033, 2099, 2100, 2104, 2118, 2119, 2121, 2130, 2132, 2133, 2136, 2137, 2141, 2147, 2158, 2160, 2165, 2166, 2170, 2171, 2172, 2176, 2190, 2193, 2194, 2196, 2199, 2203, 2204, 2209], "blocked_autorang": 4, "variablil": 4, "until": [4, 6, 8, 24, 25, 29, 31, 34, 40, 50, 55, 59, 67, 68, 85, 87, 485, 1039, 1040, 1044, 1079, 1199, 1336, 1388, 1428, 1434, 1435, 1444, 1445, 1688, 1762, 1772, 1865, 1867, 1872, 2017, 2053, 2054, 2070, 2104, 2112, 2118, 2128, 2133, 2138, 2139, 2149, 2155, 2159, 2165, 2167, 2172, 2188, 2191, 2194], "iqr": 4, "smaller": [4, 20, 24, 33, 68, 498, 544, 1026, 1552, 1772, 1877, 1894, 1939, 1985, 2133, 2149, 2157, 2158, 2189, 2206, 2209], "reach": [4, 8, 9, 10, 24, 25, 29, 30, 40, 50, 55, 60, 1388, 1772, 1865, 1868, 1872, 1873, 2130, 2136, 2146, 2161, 2165, 2192], "At": [4, 6, 7, 8, 16, 18, 20, 24, 33, 35, 1179, 1496, 1497, 1498, 1509, 1510, 1511, 1512, 1513, 1514, 1549, 1550, 1551, 2037, 2041, 2110, 2132, 2141, 2160, 2165, 2171, 2191, 2192, 2194, 2204], "high": [4, 5, 8, 9, 10, 11, 16, 25, 29, 33, 38, 40, 47, 49, 51, 68, 118, 1501, 1629, 1906, 1907, 1939, 2041, 2098, 2108, 2113, 2115, 2118, 2136, 2141, 2142, 2144, 2145, 2146, 2157, 2160, 2161, 2165, 2170, 2175, 2176, 2177, 2181, 2185, 2187, 2188, 2191, 2198, 2203, 2204, 2207, 2208], "pseudo": [4, 86], "block_siz": [4, 834, 844, 847, 2104, 2126], "enough_data": 4, "len": [4, 24, 31, 68, 70, 75, 217, 544, 703, 705, 706, 709, 1164, 1168, 1171, 1175, 1178, 1182, 1213, 1319, 1382, 1403, 1415, 1473, 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840, 845, 850, 904, 972, 1125, 1192, 1276, 1277, 1278, 1418, 1501, 1540, 1632, 1866, 1867, 1875, 1959, 1979, 1980, 1981, 1982, 1983, 1984, 2098, 2121, 2128, 2130, 2141, 2156, 2163, 2173, 2198], "total_tim": 4, "choic": [4, 9, 10, 15, 29, 33, 1331, 1379, 1583, 1592, 1902, 2132, 2155, 2163, 2170, 2190, 2191, 2204], "block": [4, 8, 9, 21, 24, 29, 31, 33, 34, 37, 50, 53, 59, 67, 68, 87, 485, 582, 583, 584, 844, 862, 964, 980, 1039, 1044, 1101, 1336, 1388, 1445, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1558, 1559, 1560, 1561, 1562, 1563, 1585, 1588, 1626, 1627, 1628, 1629, 1630, 1634, 1682, 1758, 1772, 1979, 1980, 1981, 2054, 2097, 2099, 2100, 2113, 2118, 2121, 2122, 2126, 2130, 2133, 2135, 2139, 2141, 2155, 2160, 2165, 2170, 2182, 2201, 2203, 2206, 2208], "qualiti": [4, 8, 25], "balanc": [4, 1001], "compet": [4, 2146], "amort": 4, "invoc": [4, 6, 68, 870, 1001, 1006, 1227, 1318, 1331, 2018, 2097, 2100, 2133, 2136, 2143, 2165, 2167, 2183, 2188, 2201, 2203], "less": [4, 7, 8, 15, 20, 24, 25, 29, 36, 38, 51, 53, 55, 59, 363, 680, 837, 937, 939, 948, 949, 1026, 1097, 1115, 1145, 1189, 1193, 1202, 1340, 1352, 1388, 1404, 1405, 1486, 1542, 1596, 1605, 1614, 1620, 1819, 1915, 2025, 2027, 2079, 2095, 2098, 2100, 2121, 2133, 2136, 2139, 2146, 2147, 2156, 2160, 2191, 2194, 2201], "bias": [4, 25, 744, 770, 806, 1496, 1497, 1498, 1533, 1534, 1536, 1544, 1545, 1546, 1552, 1553, 1554, 1598, 1600, 1622, 1642, 1643, 2157], "trivial": [4, 33, 36, 40, 43, 57, 745, 991, 1222, 1229, 1789, 1982, 2155, 2167, 2191, 2193], "low": [4, 8, 19, 25, 33, 38, 59, 498, 940, 1239, 1259, 1501, 1885, 1902, 1906, 1907, 1924, 1998, 2098, 2113, 2115, 2118, 2133, 2146, 2147, 2159, 2172, 2177, 2191, 2197, 2198, 2205], "digit": [4, 985, 1943, 2095, 2111, 2143, 2147], "microsecond": [4, 2133], "bia": [4, 10, 29, 35, 55, 70, 722, 723, 724, 725, 726, 727, 728, 729, 730, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 748, 749, 750, 751, 752, 753, 759, 761, 762, 763, 764, 766, 770, 771, 774, 775, 782, 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2161, 2197], "main": [4, 8, 24, 25, 29, 30, 31, 36, 38, 40, 41, 42, 43, 48, 50, 51, 53, 54, 55, 68, 259, 495, 834, 1001, 1132, 1133, 1134, 1135, 1136, 1378, 1690, 1976, 2024, 2025, 2026, 2027, 2095, 2097, 2113, 2118, 2120, 2121, 2129, 2130, 2133, 2135, 2136, 2137, 2139, 2144, 2146, 2150, 2151, 2152, 2160, 2165, 2166, 2175, 2176, 2179, 2182, 2184, 2189, 2191, 2194, 2202, 2203], "collect_callgrind": 4, "collect_baselin": 4, "retain_out_fil": 4, "callgrindstat": 4, "tupl": [4, 6, 13, 15, 20, 24, 29, 31, 35, 36, 37, 40, 47, 48, 50, 55, 56, 57, 59, 68, 232, 318, 444, 446, 448, 492, 496, 519, 536, 559, 582, 583, 584, 703, 705, 706, 707, 709, 745, 776, 777, 778, 779, 782, 783, 784, 789, 795, 796, 797, 805, 809, 812, 831, 832, 834, 844, 862, 883, 890, 891, 907, 908, 915, 916, 917, 918, 919, 921, 934, 935, 937, 938, 939, 940, 941, 942, 943, 948, 949, 952, 953, 954, 968, 984, 995, 1003, 1011, 1018, 1025, 1041, 1051, 1052, 1055, 1068, 1089, 1095, 1124, 1125, 1143, 1146, 1148, 1162, 1164, 1165, 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1835, 1875, 1905, 1907, 1909, 1936, 1979, 1980, 1981, 1982, 1983, 1984, 1993, 2074, 2094, 2100, 2101, 2120, 2121, 2133, 2135, 2136, 2139, 2144, 2152, 2155, 2158, 2160, 2165, 2167, 2170, 2172, 2173, 2175, 2177, 2190, 2193, 2194, 2202, 2203, 2204, 2211], "jitter": 4, "interpret": [4, 24, 29, 34, 38, 40, 50, 53, 55, 56, 57, 232, 789, 795, 909, 943, 1041, 1100, 1166, 1167, 1168, 1173, 1175, 1176, 1177, 1178, 1199, 1227, 1266, 1278, 1315, 1320, 1327, 1331, 1368, 1388, 1521, 1646, 1688, 1699, 1700, 1759, 1936, 1941, 2095, 2099, 2100, 2104, 2118, 2132, 2133, 2170, 2172, 2174, 2191, 2192, 2203], "ideal": [4, 49, 51, 1331, 1863, 2120, 2121, 2194, 2203], "analysi": [4, 25, 38, 55, 57, 68, 1335, 1885, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2101, 2108, 2144, 2151, 2152, 2181, 2189, 2191, 2192, 2193, 2197], "valgrind": 4, "degrad": [4, 15, 59, 2132, 2135, 2136, 2170], "due": [4, 5, 6, 8, 25, 29, 31, 33, 38, 49, 59, 64, 68, 70, 72, 485, 909, 937, 938, 943, 1022, 1219, 1328, 1351, 1352, 1379, 1405, 1521, 1535, 1626, 1635, 1699, 1740, 1823, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 1998, 2097, 2133, 2141, 2142, 2147, 2148, 2149, 2155, 2160, 2161, 2165, 2167, 2188, 2194, 2199, 2203, 2204, 2206, 2208], "amelior": 4, "suffici": [4, 15, 25, 29, 38, 42, 50, 51, 56, 59, 485, 1013, 1843, 1844, 1862, 1998, 2095, 2104, 2140, 2170, 2173, 2194], "callgrind_control": 4, "callgrind_annot": 4, "boundari": [4, 33, 35, 68, 789, 795, 985, 1269, 1503, 1504, 1505, 1506, 1507, 1508, 1604, 1605, 1606, 1607, 1608, 1609, 1635, 1638, 1639, 1640, 1699, 1759, 1868, 1875, 2098, 2121, 2135, 2158, 2165, 2194], "caller": [4, 40, 50, 68, 883, 1315, 1582, 1630, 1880, 1982, 2104, 2130, 2133, 2165, 2167], "structur": [4, 6, 10, 13, 24, 25, 29, 33, 35, 39, 40, 43, 51, 55, 56, 57, 59, 68, 70, 71, 75, 79, 921, 934, 935, 967, 1001, 1018, 1196, 1209, 1214, 1227, 1234, 1235, 1331, 1582, 1642, 1802, 1806, 1838, 1885, 1998, 2048, 2099, 2101, 2104, 2107, 2113, 2126, 2133, 2135, 2136, 2137, 2139, 2146, 2149, 2150, 2155, 2157, 2158, 2165, 2172, 2175, 2176, 2177, 2179, 2181, 2184, 2190, 2191, 2192, 2194, 2195, 2199, 2206], "restrict": [4, 9, 13, 24, 38, 56, 59, 64, 65, 1003, 1018, 1387, 1410, 1517, 2099, 2100, 2101, 2120, 2121, 2130, 2133, 2141, 2149, 2160, 2190], "builtin": [4, 29, 68, 70, 79, 1328, 1387, 1911, 2005, 2097, 2099, 2101, 2165, 2167, 2191, 2194, 2203, 2204], "surpris": [4, 9, 59, 2095, 2141, 2147, 2193], "serial": [4, 16, 24, 29, 31, 34, 50, 1315, 1325, 1326, 1387, 1582, 1762, 1880, 1927, 2095, 2096, 2098, 2111, 2120, 2130, 2133, 2139, 2143, 2144, 2146, 2152, 2155, 2158, 2160, 2165, 2166, 2172], "subsequ": [4, 8, 15, 16, 18, 29, 55, 57, 59, 68, 1001, 1040, 1075, 1315, 1327, 1331, 1509, 1510, 1511, 1512, 1513, 1514, 1582, 1611, 1880, 2121, 2133, 2148, 2155, 2159, 2165, 2170, 2183, 2184, 2188, 2194], "deseri": [4, 31, 1229, 1387, 1762, 2095, 2111, 2149, 2165, 2172], "globalsbridg": 4, "care": [4, 8, 15, 29, 38, 59, 67, 68, 1166, 1168, 1228, 1229, 1582, 2118, 2132, 2133, 2135, 2136, 2138, 2144, 2146, 2149, 2155, 2165, 2170, 2188, 2193, 2194, 2195], "reli": [4, 10, 15, 24, 25, 35, 40, 43, 59, 68, 149, 922, 948, 1145, 1518, 1863, 2109, 2130, 2132, 2135, 2136, 2139, 2148, 2149, 2170, 2174, 2193, 2194, 2195, 2197], "pickl": [4, 24, 25, 29, 1315, 1323, 1387, 1582, 1880, 1927, 2095, 2111, 2118, 2149, 2153, 2158, 2165, 2206], "transfer": [4, 24, 29, 59, 2110, 2118, 2133, 2142, 2144, 2157, 2158, 2165, 2173], "profil": [4, 5, 18, 20, 47, 68, 485, 956, 957, 964, 1001, 1056, 1329, 1460, 1765, 1766, 1767, 1768, 1943, 2096, 2115, 2139, 2155, 2156, 2165, 2182, 2183, 2194, 2203], "empti": [4, 13, 28, 29, 31, 36, 40, 48, 55, 56, 57, 59, 64, 68, 232, 320, 512, 514, 522, 536, 544, 831, 953, 968, 971, 972, 982, 988, 1018, 1145, 1147, 1313, 1315, 1316, 1341, 1361, 1362, 1367, 1374, 1397, 1398, 1399, 1400, 1407, 1415, 1452, 1495, 1517, 1525, 1582, 1589, 1661, 1680, 1727, 1823, 1835, 1836, 1880, 1982, 1997, 2014, 2094, 2097, 2098, 2099, 2100, 2102, 2110, 2112, 2119, 2120, 2128, 2131, 2133, 2136, 2148, 2149, 2156, 2158, 2170, 2171, 2176, 2179, 2190, 2191, 2193, 2198, 2201], "drive": [4, 10, 29, 2130], "facil": [4, 26, 1387, 2118], "analyz": [4, 5, 19, 55, 57, 68, 2135, 2136, 2139, 2147, 2149, 2204], "manipul": [4, 20, 59, 67, 1319, 2129, 2138, 2144, 2163, 2172, 2202], "1000000": [4, 1839], "mirror": [4, 138, 1772], "semant": [4, 10, 12, 18, 29, 36, 37, 50, 51, 55, 56, 59, 66, 68, 87, 149, 806, 903, 904, 905, 922, 943, 983, 1044, 1101, 1203, 1214, 1227, 1242, 1243, 1319, 1326, 1385, 1445, 1495, 1661, 1733, 1781, 1782, 1863, 2033, 2048, 2096, 2099, 2100, 2104, 2107, 2121, 2137, 2144, 2155, 2165, 2170, 2183, 2194], "number_per_run": 4, "raw_tim": 4, "task_spec": 4, "serializ": [4, 25, 2097], "consum": [4, 24, 31, 35, 39, 53, 56, 67, 434, 1145, 2118, 2133, 2139, 2146, 2151, 2155, 2175], "extrapol": 4, "sinc": [4, 8, 15, 24, 25, 29, 31, 33, 37, 38, 43, 51, 53, 55, 57, 59, 68, 314, 487, 533, 757, 795, 805, 822, 823, 833, 991, 1092, 1094, 1097, 1145, 1185, 1186, 1187, 1202, 1312, 1315, 1320, 1326, 1329, 1405, 1494, 1524, 1578, 1579, 1580, 1582, 1635, 1643, 1653, 1688, 1690, 1759, 1762, 1806, 1825, 1830, 1838, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1993, 2076, 2077, 2079, 2097, 2098, 2099, 2100, 2104, 2106, 2110, 2113, 2121, 2129, 2130, 2133, 2136, 2137, 2138, 2139, 2141, 2143, 2144, 2149, 2150, 2151, 2155, 2156, 2158, 2159, 2160, 2164, 2165, 2166, 2167, 2170, 2171, 2173, 2174, 2176, 2177, 2185, 2188, 2191, 2194, 2199, 2202, 2203, 2204], "properti": [4, 24, 29, 30, 31, 34, 35, 36, 38, 42, 47, 50, 55, 56, 59, 68, 804, 909, 935, 952, 962, 1041, 1070, 1161, 1162, 1164, 1166, 1167, 1168, 1176, 1177, 1178, 1315, 1490, 1582, 1616, 1641, 1746, 1762, 1773, 1774, 1775, 1776, 1816, 1880, 2065, 2100, 2104, 2113, 2126, 2130, 2133, 2136, 2141, 2152, 2157, 2159, 2165, 2170, 2172, 2173, 2176, 2179, 2184, 2190, 2191, 2192, 2193, 2205, 2209], "significant_figur": 4, "figur": [4, 8, 9, 31, 36, 68, 957, 2132, 2135, 2136, 2153, 2155, 2167, 2175, 2181, 2191, 2194, 2208], "intend": [4, 31, 50, 55, 57, 68, 919, 929, 932, 934, 1046, 1229, 1335, 1653, 1765, 1766, 1767, 1768, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 2100, 2128, 2130, 2144, 2155, 2158, 2203], "interquartil": 4, "mitig": [4, 70, 2139, 2147, 2172], "tail": [4, 24, 48], "645": 4, "conjunct": [4, 24, 29, 34, 59, 834, 842, 1653, 1688, 1772, 2163, 2165], "trim_sigfig": 4, "human": [4, 14, 35, 48, 55, 1088, 1102, 2128, 2155, 2192, 2205], "raw": [4, 68, 1199, 1374, 2104, 2133, 2158, 2159], "built_with_debug_symbol": 4, "baseline_inclusive_stat": 4, "baseline_exclusive_stat": 4, "stmt_inclusive_stat": 4, "stmt_exclusive_stat": 4, "stmt_callgrind_out": 4, "done": [4, 16, 24, 29, 31, 33, 34, 35, 36, 38, 48, 56, 59, 67, 68, 485, 495, 680, 770, 948, 955, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1331, 1405, 1496, 1497, 1498, 1518, 1521, 1533, 1544, 1545, 1546, 1556, 1557, 1564, 1565, 1566, 1567, 1592, 1622, 1628, 1630, 1680, 1772, 1865, 1872, 1880, 1988, 2100, 2110, 2112, 2118, 2130, 2132, 2133, 2135, 2136, 2137, 2138, 2144, 2147, 2152, 2153, 2155, 2157, 2160, 2161, 2165, 2167, 2173, 2189, 2190, 2191, 2193, 2200, 2202], "functioncount": 4, "stat": [4, 62, 812, 834, 850, 863, 864, 1101, 1109, 1110, 1111, 1622, 1772, 2083, 2084, 2113, 2133, 2180, 2194, 2204], "as_standard": 4, "strip": [4, 1690, 1772, 2097, 2156, 2170], "prefix": [4, 29, 31, 40, 50, 59, 68, 758, 767, 1315, 1582, 1772, 1880, 2095, 2106, 2130, 2155, 2158, 2160, 2180, 2201], "stumbl": 4, "filepath": 4, "dif": 4, "compon": [4, 8, 10, 16, 19, 20, 29, 36, 38, 68, 680, 923, 948, 1003, 1165, 1166, 1168, 1176, 1177, 1178, 1355, 1626, 1627, 1628, 1629, 1630, 1762, 1885, 1993, 2046, 2047, 2101, 2106, 2121, 2126, 2130, 2135, 2136, 2143, 2144, 2170, 2172, 2183, 2189, 2195, 2203, 2204], "locat": [4, 10, 15, 29, 31, 35, 38, 47, 55, 57, 153, 253, 485, 512, 680, 908, 983, 985, 1053, 1124, 1125, 1337, 1387, 1413, 1418, 1421, 1468, 1518, 1528, 1617, 1634, 1653, 1688, 1755, 1758, 1772, 1795, 1896, 1931, 1952, 1974, 1978, 2036, 2095, 2097, 2111, 2126, 2133, 2149, 2158, 2159, 2165, 2167, 2170, 2175, 2180, 2184, 2188, 2193, 2203], "someth": [4, 8, 55, 56, 61, 68, 937, 939, 1145, 1229, 1239, 1315, 1331, 1582, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 2008, 2102, 2113, 2118, 2119, 2121, 2130, 2142, 2150, 2155, 2165, 2189, 2190, 2191, 2194, 2196, 2202, 2203, 2204, 2205], "resembl": [4, 16, 2184], "23234231": 4, "first_build_dir": 4, "foo": [4, 15, 29, 47, 48, 55, 67, 68, 70, 78, 79, 834, 842, 883, 888, 1001, 1013, 1202, 1203, 1212, 1313, 1318, 1323, 1326, 1327, 1330, 1331, 1825, 2037, 2039, 2095, 2097, 2099, 2100, 2104, 2108, 2136, 2144, 2155, 2158, 2177, 2188, 2191, 2194, 2203, 2204], "9823794": 4, "bar": [4, 8, 47, 55, 68, 70, 78, 79, 883, 888, 1013, 1026, 1202, 1318, 1326, 1991, 1992, 2043, 2044, 2095, 2097, 2099, 2104, 2111, 2144, 2155, 2158, 2177], "53453": 4, "src": [4, 29, 68, 195, 227, 312, 320, 470, 511, 512, 513, 514, 515, 516, 518, 537, 1136, 1203, 1626, 1629, 1630, 1836, 1928, 1929, 1930, 1934, 1964, 2010, 2036, 2098, 2115, 2121, 2158, 2198], "function_that_actually_chang": 4, "second_build_dir": 4, "cancel": [4, 1312], "site": [4, 8, 2184], "denois": 4, "explan": [4, 10, 21, 26, 43, 891, 1315, 1582, 1880, 2135, 2136, 2144, 2194, 2200, 2204], "delta": [4, 38, 770, 1026, 1533, 1542, 1552, 1614, 1697, 1840, 1991, 1992, 2021, 2043, 2044, 2098, 2128], "inclus": [4, 38, 55, 86, 512, 1233, 1239, 1276, 1278, 1386, 1402, 1408, 1644, 1645, 1906, 1907, 1990, 2164, 2177], "diff": [4, 8, 2097, 2098, 2121, 2156], "One": [4, 9, 15, 29, 31, 55, 56, 62, 64, 68, 582, 980, 1003, 1203, 1214, 1306, 1635, 1726, 1773, 1781, 1782, 1789, 1793, 1863, 1868, 1877, 1906, 1907, 2031, 2048, 2097, 2099, 2100, 2120, 2131, 2132, 2133, 2136, 2143, 2149, 2159, 2167, 2175, 2185, 2189, 2190, 2192, 2193, 2201, 2208], "reason": [4, 9, 10, 24, 29, 31, 33, 40, 55, 59, 64, 680, 812, 870, 934, 935, 1166, 1168, 1315, 1331, 1332, 1351, 1352, 1379, 1416, 1494, 1521, 1582, 1690, 1740, 1795, 1880, 1927, 2097, 2099, 2100, 2104, 2106, 2121, 2130, 2135, 2137, 2139, 2147, 2149, 2155, 2165, 2173, 2189, 2190, 2191, 2193, 2194, 2197, 2203, 2204, 2205], "unit": [4, 12, 15, 38, 40, 51, 59, 68, 770, 771, 1163, 1181, 1490, 1500, 1523, 1526, 1531, 1532, 1533, 1534, 1601, 1602, 1612, 1653, 1677, 1681, 1686, 1687, 1734, 1743, 1799, 1800, 1804, 1807, 1809, 1812, 1908, 2023, 2078, 2114, 2130, 2132, 2139, 2141, 2158, 2187, 2188], "next": [4, 24, 29, 33, 35, 38, 50, 55, 56, 57, 59, 68, 559, 770, 1227, 1319, 1482, 1534, 1553, 1600, 1772, 1818, 2118, 2129, 2130, 2132, 2133, 2136, 2137, 2139, 2144, 2146, 2151, 2159, 2165, 2166, 2170, 2172, 2173, 2175, 2184, 2188, 2191, 2192, 2194], "logic": [4, 6, 13, 15, 24, 37, 41, 54, 57, 59, 68, 973, 975, 976, 978, 1008, 1009, 1010, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1203, 1229, 1319, 1397, 1398, 1399, 1400, 1410, 1588, 1772, 1894, 1921, 2007, 2100, 2101, 2133, 2135, 2136, 2137, 2141, 2160, 2191], "question": [4, 11, 24, 68, 1237, 1827, 2096, 2130, 2139, 2182, 2190, 2193], "involv": [4, 6, 8, 10, 12, 24, 29, 57, 59, 62, 64, 68, 1225, 1772, 1939, 2100, 2119, 2121, 2130, 2133, 2135, 2138, 2144, 2147, 2155, 2160, 2165, 2166, 2167, 2170, 2190, 2193, 2201, 2203], "look": [4, 5, 8, 9, 10, 13, 16, 29, 36, 38, 49, 50, 55, 56, 57, 61, 64, 68, 70, 80, 81, 485, 891, 937, 939, 1018, 1227, 1238, 1315, 1410, 1486, 1582, 1679, 1808, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1979, 1980, 1981, 1983, 1984, 2097, 2099, 2104, 2119, 2126, 2129, 2130, 2133, 2137, 2143, 2144, 2146, 2150, 2155, 2158, 2160, 2165, 2166, 2181, 2185, 2188, 2189, 2190, 2191, 2194, 2195, 2196, 2197, 2200, 2201, 2202, 2203, 2204, 2206], "autom": [4, 9, 68, 2097, 2160, 2184, 2194], "easili": [4, 8, 9, 12, 25, 29, 31, 35, 55, 1203, 1589, 1688, 1727, 1863, 1924, 2037, 2112, 2121, 2136, 2137, 2141, 2144, 2149, 2152, 2157, 2164, 2165, 2167, 2175, 2194, 2202, 2203, 2204], "exclus": [4, 24, 29, 38, 40, 50, 59, 68, 745, 1278, 1772, 1906, 1907, 1910, 2130, 2177], "basi": [4, 10, 11, 38, 680, 1388, 1868, 2133, 2143, 2160, 2165, 2183, 2188, 2203], "thought": [4, 47, 68, 1163, 1165, 1181, 1233, 2191], "path_and_function_nam": 4, "children": [4, 35, 43, 59, 68, 803, 1315, 1582, 1880, 2118, 2139, 2144, 2158, 2167], "identifi": [4, 8, 10, 29, 31, 40, 44, 47, 50, 51, 53, 68, 85, 485, 831, 1278, 1387, 1444, 1622, 2101, 2104, 2118, 2131, 2143, 2144, 2149, 2158, 2165, 2166, 2167, 2172, 2175, 2183, 2190, 2201, 2203], "hot": [4, 38, 1690, 1726, 2141, 2188], "spot": [4, 1781, 1782], "_data": 4, "truncate_row": 4, "_linewidth": 4, "subtract": [4, 312, 563, 894, 1199, 1690, 1979, 1980, 1981, 1983, 1984, 1994, 2098, 2156, 2170], "index": [4, 16, 20, 24, 25, 29, 31, 35, 38, 48, 55, 57, 68, 190, 207, 280, 311, 312, 313, 314, 315, 316, 318, 320, 321, 470, 471, 511, 512, 513, 514, 515, 516, 517, 518, 683, 684, 685, 689, 690, 691, 705, 706, 831, 883, 906, 921, 923, 934, 935, 966, 985, 1014, 1058, 1061, 1095, 1124, 1125, 1165, 1206, 1207, 1208, 1212, 1214, 1256, 1287, 1288, 1289, 1290, 1313, 1332, 1337, 1345, 1363, 1381, 1395, 1403, 1405, 1409, 1413, 1416, 1417, 1418, 1421, 1447, 1450, 1468, 1474, 1477, 1478, 1486, 1501, 1517, 1524, 1525, 1583, 1584, 1589, 1592, 1593, 1632, 1634, 1679, 1680, 1726, 1801, 1803, 1810, 1811, 1816, 1829, 1865, 1866, 1867, 1868, 1869, 1871, 1872, 1873, 1874, 1875, 1878, 1879, 1881, 1896, 1928, 1929, 1930, 1931, 1933, 1934, 1935, 1964, 1975, 1976, 1979, 1980, 1981, 1983, 1984, 1993, 2010, 2031, 2032, 2034, 2035, 2036, 2039, 2048, 2056, 2058, 2096, 2097, 2098, 2100, 2104, 2107, 2118, 2119, 2120, 2126, 2130, 2133, 2135, 2137, 2140, 2142, 2148, 2156, 2157, 2167, 2169, 2170, 2172, 2173, 2174, 2175, 2176, 2177, 2193, 2194, 2198, 2203, 2204], "cpython": [4, 15, 55, 68, 2182, 2192], "known": [4, 8, 10, 23, 28, 29, 34, 44, 50, 56, 60, 63, 65, 70, 80, 81, 1209, 1210, 1229, 1331, 1332, 1387, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1552, 1598, 1612, 1614, 1634, 1699, 1743, 1762, 1951, 1952, 1998, 2014, 2098, 2102, 2104, 2113, 2118, 2121, 2128, 2130, 2132, 2148, 2155, 2157, 2165, 2167, 2171, 2182, 2190, 2195, 2204], "quit": [4, 8, 68, 1781, 1782, 2100, 2126, 2136, 2138, 2158, 2165, 2201], "noisi": 4, "higher": [4, 8, 9, 25, 29, 33, 59, 60, 65, 70, 75, 79, 87, 149, 922, 924, 943, 1044, 1089, 1101, 1137, 1204, 1210, 1214, 1223, 1239, 1242, 1276, 1292, 1445, 1475, 1517, 1574, 1589, 1740, 1840, 1896, 1998, 2048, 2054, 2104, 2133, 2136, 2137, 2141, 2143, 2149, 2160, 2161, 2165, 2173, 2190, 2203], "filter": [4, 21, 543, 782, 783, 784, 1312, 1335, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1993, 2100, 2158, 2204], "transform": [4, 13, 24, 31, 35, 36, 37, 55, 59, 64, 66, 414, 791, 810, 865, 867, 886, 887, 992, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1202, 1203, 1204, 1208, 1213, 1236, 1312, 1325, 1499, 1536, 1544, 1545, 1546, 1554, 1569, 1588, 1611, 1627, 1628, 1629, 1630, 1653, 1659, 1688, 1705, 1725, 1762, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1855, 1859, 1860, 1861, 1862, 1863, 1959, 1993, 2108, 2121, 2137, 2139, 2140, 2160, 2170, 2175, 2181, 2182, 2184, 2192, 2196, 2203], "rather": [4, 9, 10, 15, 29, 40, 43, 53, 55, 57, 59, 68, 70, 79, 789, 795, 961, 991, 1014, 1313, 1316, 1387, 1478, 1653, 1688, 1699, 1759, 1790, 1939, 1943, 2097, 2100, 2120, 2121, 2131, 2133, 2136, 2137, 2155, 2158, 2160, 2165, 2170, 2175, 2188, 2189, 2191, 2193, 2194, 2195, 2196, 2203], "unicod": [4, 2101], "dictionari": [4, 15, 24, 25, 31, 35, 36, 38, 55, 63, 68, 414, 680, 804, 805, 806, 810, 829, 830, 831, 865, 867, 868, 883, 887, 893, 968, 1001, 1101, 1202, 1212, 1222, 1229, 1315, 1316, 1323, 1327, 1331, 1332, 1387, 1388, 1458, 1459, 1464, 1524, 1525, 1582, 1583, 1592, 1679, 1806, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 2063, 2081, 2082, 2099, 2101, 2136, 2144, 2155, 2157, 2165, 2175, 2180, 2199, 2205, 2206], "lookup": [4, 31, 38, 50, 1524, 1679, 2097, 2101, 2132, 2166, 2197], "map": [4, 15, 29, 31, 36, 38, 40, 47, 48, 50, 51, 55, 56, 57, 59, 64, 65, 68, 70, 79, 232, 694, 745, 804, 805, 809, 810, 813, 828, 829, 831, 837, 867, 868, 883, 884, 885, 886, 887, 893, 914, 982, 1044, 1197, 1203, 1214, 1225, 1227, 1229, 1234, 1269, 1278, 1323, 1326, 1387, 1388, 1512, 1513, 1514, 1520, 1521, 1522, 1526, 1528, 1578, 1579, 1580, 1583, 1592, 1674, 1675, 1676, 1681, 1789, 1806, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1896, 1898, 2032, 2033, 2048, 2054, 2101, 2104, 2112, 2120, 2122, 2130, 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2208], "filter_fn": 4, "map_fn": 4, "coalesc": [4, 322, 327, 543, 613, 1052, 1978, 1982, 2031, 2098, 2135, 2156, 2170, 2194], "entri": [4, 20, 29, 30, 34, 38, 40, 41, 48, 50, 51, 314, 745, 887, 888, 961, 1086, 1087, 1135, 1186, 1187, 1290, 1315, 1336, 1524, 1525, 1582, 1588, 1679, 1680, 1801, 1802, 1810, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1878, 1879, 1880, 1881, 1975, 2097, 2100, 2101, 2107, 2121, 2130, 2135, 2136, 2141, 2144, 2149, 2163, 2170, 2175, 2180, 2189, 2190, 2191, 2192], "color": [4, 68, 1546, 2099, 2100, 2175, 2195, 2203], "rowwis": [4, 37], "columnwis": 4, "extend_result": 4, "highlight_warn": 4, "highlight": [4, 56, 740, 741, 2100], "trim_significant_figur": 4, "trim": [4, 970, 979, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1273, 1274, 1312], "h": [5, 11, 15, 35, 38, 486, 487, 751, 752, 753, 770, 993, 994, 1345, 1352, 1355, 1365, 1379, 1484, 1485, 1492, 1493, 1495, 1497, 1498, 1510, 1511, 1513, 1521, 1522, 1524, 1526, 1533, 1534, 1545, 1546, 1552, 1553, 1554, 1565, 1566, 1576, 1577, 1594, 1595, 1598, 1600, 1617, 1633, 1636, 1637, 1653, 1661, 1688, 1724, 1730, 1731, 1740, 1789, 1790, 1824, 1836, 1885, 1997, 1998, 2098, 2115, 2119, 2120, 2126, 2130, 2133, 2135, 2136, 2138, 2141, 2155, 2174, 2175, 2176, 2184, 2190, 2200], "finit": [5, 38, 948, 949, 1304, 1305, 1351, 1352, 1362, 1363, 1379, 1405, 1468, 1472, 1494, 1997, 2136, 2141, 2177], "natur": [5, 8, 9, 12, 31, 38, 59, 70, 72, 948, 949, 1344, 1349, 1375, 1389, 1391, 1396, 1486, 1630, 1740, 2139, 2141, 2151, 2152, 2170, 2171], "against": [5, 6, 15, 29, 40, 50, 57, 806, 894, 948, 949, 1173, 1174, 1175, 1179, 1180, 1182, 1256, 1306, 1315, 1331, 1332, 1582, 1588, 1837, 1880, 2095, 2100, 2158, 2181, 2191, 2200], "cprofil": 5, "mode": [5, 9, 24, 25, 29, 31, 33, 38, 50, 53, 55, 57, 64, 65, 68, 220, 221, 487, 756, 757, 782, 783, 784, 789, 795, 796, 797, 829, 831, 841, 850, 888, 889, 918, 920, 924, 927, 928, 929, 934, 935, 937, 938, 939, 940, 944, 945, 946, 948, 1001, 1006, 1014, 1037, 1076, 1119, 1120, 1149, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1203, 1206, 1207, 1208, 1209, 1212, 1213, 1215, 1216, 1223, 1228, 1263, 1298, 1299, 1313, 1315, 1316, 1319, 1331, 1333, 1374, 1387, 1417, 1434, 1435, 1465, 1496, 1497, 1498, 1509, 1510, 1511, 1525, 1536, 1544, 1545, 1546, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1582, 1599, 1622, 1635, 1636, 1663, 1664, 1665, 1680, 1688, 1699, 1727, 1740, 1759, 1760, 1761, 1774, 1783, 1785, 1790, 1795, 1828, 1868, 1877, 1880, 1895, 1938, 1940, 2036, 2098, 2104, 2110, 2119, 2128, 2133, 2135, 2137, 2138, 2139, 2140, 2144, 2151, 2152, 2156, 2159, 2161, 2162, 2163, 2165, 2176, 2179, 2184, 2188, 2192, 2193, 2194, 2195, 2196, 2198, 2203, 2204, 2205], "correct": [5, 6, 7, 8, 25, 29, 30, 31, 35, 36, 38, 50, 55, 57, 87, 204, 208, 220, 554, 579, 602, 603, 614, 622, 929, 930, 934, 949, 1026, 1166, 1168, 1176, 1177, 1178, 1234, 1290, 1313, 1316, 1331, 1332, 1351, 1445, 1518, 1535, 1547, 1762, 1772, 1816, 1942, 1991, 1992, 2043, 2044, 2097, 2098, 2099, 2104, 2108, 2119, 2120, 2128, 2133, 2136, 2141, 2157, 2172, 2191, 2198, 2202, 2203], "launch": [5, 15, 24, 32, 35, 36, 40, 42, 43, 48, 49, 50, 54, 67, 1040, 1075, 1772, 2130, 2132, 2133, 2135, 2136, 2165, 2188, 2196], "spent": [5, 29, 962, 1875, 2132, 2144, 2194, 2201, 2204], "appear": [5, 25, 29, 38, 55, 56, 68, 883, 964, 1089, 1145, 1186, 1187, 1214, 1387, 1421, 1423, 1773, 1774, 1917, 1945, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2048, 2097, 2100, 2119, 2120, 2136, 2137, 2144, 2155, 2158, 2191, 2194, 2201], "extrem": [5, 29, 1772, 2130, 2155, 2193], "expens": [5, 24, 38, 59, 1789, 2126, 2133, 2141, 2143, 2159, 2165, 2176, 2190, 2194, 2197, 2200, 2204], "bound": [5, 16, 25, 29, 56, 59, 480, 807, 985, 996, 1159, 1160, 1229, 1233, 1238, 1315, 1491, 1492, 1493, 1575, 1576, 1577, 1582, 1601, 1688, 1877, 1880, 1910, 1931, 2100, 2101, 2128, 2130, 2139, 2158, 2160, 2171, 2190, 2191, 2193, 2203, 2204], "greater": [5, 20, 29, 50, 70, 71, 291, 616, 680, 977, 990, 991, 996, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1258, 1272, 1370, 1439, 1494, 1631, 1688, 1699, 1726, 1740, 1756, 1759, 1790, 1824, 1877, 2036, 2098, 2121, 2130, 2133, 2148, 2156, 2170, 2171], "spend": [5, 8, 20, 35, 1388, 2108, 2197], "sens": [5, 38, 50, 68, 1806, 2032, 2033, 2100, 2130, 2139], "respons": [5, 8, 10, 15, 29, 31, 34, 35, 36, 38, 40, 47, 53, 59, 67, 1003, 1040, 1075, 1231, 1570, 1706, 1772, 1982, 2069, 2104, 2130, 2133, 2136, 2137, 2144, 2165, 2193, 2203], "Of": [5, 1829, 2095, 2135, 2136, 2182, 2191, 2194], "cours": [5, 20, 68, 1217, 2095, 2135, 2136, 2165, 2191, 2194], "realiti": [5, 2139, 2203], "complic": [5, 25, 37, 68, 805, 1939, 2104, 2120, 2131, 2158, 2165, 2167, 2191, 2193, 2203], "account": [5, 35, 48, 68, 1495, 1772, 2126, 2128, 2132, 2139, 2170, 2188], "heavili": [5, 1846, 2132, 2136, 2158], "similarli": [5, 8, 29, 31, 35, 55, 67, 68, 770, 802, 806, 887, 992, 1165, 1315, 1371, 1582, 1630, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 1880, 2024, 2025, 2026, 2027, 2099, 2104, 2130, 2136, 2138, 2141, 2147, 2170, 2193, 2196, 2201], "platform": [5, 9, 10, 15, 29, 42, 43, 47, 1362, 1363, 1374, 1895, 1997, 2110, 2142, 2147, 2148, 2160, 2184], "startup": [5, 20], "slower": [5, 15, 29, 35, 905, 938, 1185, 1186, 1187, 1345, 1352, 1707, 1789, 1840, 1841, 2136, 2148, 2157, 2171, 2193, 2203], "rerun": [6, 29, 2133], "segment": [6, 1101, 1199, 1218, 1614, 2133, 2158, 2194, 2199, 2203, 2206], "persist": [6, 22, 31, 55, 59, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 770, 829, 830, 831, 1315, 1533, 1552, 1582, 1598, 1773, 1775, 1880, 2144, 2149, 2150, 2172, 2183, 2188, 2193, 2197], "rng": [6, 24, 1073, 1116, 1429, 1440, 1454, 1462, 1932, 2067, 2088, 2133, 2148, 2164], "advanc": [6, 16, 24, 25, 40, 1829, 1998, 2039, 2104, 2107, 2120, 2133, 2136, 2142, 2146, 2174, 2175, 2194, 2203], "juggl": 6, "moder": 6, "hit": [6, 9, 15, 57, 1231, 1772, 2095, 2121, 2133, 2188, 2191, 2194, 2197, 2203, 2204], "preserve_rng_st": 6, "checkpoint_sequenti": 6, "omit": [6, 15, 29, 149, 1179, 1180, 1182, 1229, 1535, 1596, 2150, 2155, 2165, 2177, 2191, 2195], "exclud": [6, 10, 25, 33, 50, 68, 1011, 1278, 1525, 1680, 1729, 1837, 1939, 2024, 2025, 2026, 2027, 2130, 2142, 2149, 2158, 2166, 2192, 2194], "_infer_device_typ": 6, "remain": [6, 9, 20, 36, 38, 50, 68, 1423, 1524, 1525, 1679, 1680, 1762, 1772, 1781, 1782, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 1877, 1896, 1918, 2112, 2113, 2118, 2129, 2138, 2144, 2157, 2172, 2184, 2192], "consequ": [6, 64, 1379, 1552, 1997, 2100, 2130, 2133, 2146, 2148, 2151], "random": [6, 36, 38, 40, 50, 51, 60, 68, 86, 153, 770, 921, 934, 935, 971, 1073, 1074, 1080, 1090, 1091, 1112, 1113, 1116, 1117, 1207, 1214, 1268, 1291, 1388, 1408, 1429, 1430, 1438, 1440, 1454, 1462, 1490, 1501, 1526, 1533, 1552, 1601, 1681, 1732, 1738, 1762, 1790, 1797, 1803, 1804, 1811, 1812, 1831, 1885, 1902, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1932, 1944, 1998, 2039, 2048, 2067, 2068, 2071, 2074, 2075, 2085, 2086, 2088, 2089, 2095, 2096, 2098, 2104, 2106, 2128, 2137, 2141, 2147, 2155, 2156, 2175, 2194, 2204], "gradient": [6, 12, 24, 25, 29, 33, 34, 35, 36, 38, 59, 60, 63, 149, 220, 221, 289, 334, 486, 487, 494, 512, 705, 706, 909, 918, 920, 922, 925, 926, 927, 928, 929, 930, 931, 932, 934, 935, 937, 938, 939, 940, 941, 942, 943, 944, 945, 948, 949, 953, 954, 972, 998, 1149, 1202, 1203, 1204, 1205, 1209, 1214, 1256, 1315, 1351, 1352, 1355, 1362, 1363, 1379, 1388, 1405, 1407, 1413, 1416, 1418, 1494, 1501, 1512, 1513, 1514, 1517, 1518, 1524, 1525, 1535, 1549, 1550, 1551, 1582, 1589, 1614, 1671, 1672, 1679, 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43, 2097, 2130, 2137, 2141, 2191, 2195], "maker": 10, "strong": 10, "toward": [10, 70, 1140, 1277, 1278, 1388, 1482, 1772, 1872, 1915, 1923, 1924, 2139], "philosophi": [10, 63, 2096], "beyond": [10, 25, 57, 1439, 1517, 1843, 1844, 1859, 1922, 2115, 2138, 2144, 2190], "encourag": [10, 15, 40, 2160, 2170, 2177], "propos": [10, 1839, 1866, 1867, 1880, 2107, 2141, 2157, 2166], "review": [10, 11, 25, 2110, 2158, 2196], "willing": [10, 2183], "invest": 10, "anyon": 10, "ownership": [10, 68], "codebas": [10, 2136, 2203], "strictli": [10, 24, 149, 190, 207, 485, 922, 985, 1278, 1315, 1319, 1582, 1880, 2130, 2171], "compani": 10, "bui": 10, "addition": [10, 15, 24, 25, 29, 31, 36, 38, 40, 55, 57, 59, 149, 512, 616, 922, 957, 1203, 1207, 1208, 1319, 1379, 1486, 1544, 1545, 1546, 1973, 2138, 2179, 2203, 2206], "membership": [10, 40, 49, 50, 2101], "That": [10, 13, 18, 20, 40, 48, 51, 61, 68, 1014, 1018, 1312, 1386, 1402, 2036, 2104, 2110, 2136, 2137, 2138, 2139, 2149, 2158, 2165, 2191, 2194], "seat": 10, "reserv": [10, 33, 47, 1101, 2081, 2101, 2133, 2144, 2206], "emploi": [10, 31, 1862, 2144, 2158, 2184], "directori": [10, 15, 29, 31, 35, 40, 48, 1837, 2095, 2111, 2143, 2155, 2158, 2159, 2175, 2184, 2185, 2194, 2204], "procedur": [10, 38, 888, 1331, 1332, 1388, 1971, 2165, 2204], "disput": 10, "made": [10, 18, 21, 29, 31, 34, 51, 55, 68, 929, 932, 934, 949, 1042, 1315, 1582, 1628, 1630, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1851, 1853, 1859, 1860, 1861, 1862, 1863, 1880, 1912, 2099, 2107, 2130, 2150, 2158, 2175, 2177, 2188, 2190, 2191, 2203, 2204, 2205], "public": [10, 11, 36, 1203, 2121, 2136, 2203, 2205], "resolut": [10, 1594, 1595, 1653, 1688, 1730, 1731, 2021, 2101, 2158, 2209], "conclus": 10, "publicli": [10, 2205], "vision": [10, 1325, 1517, 1671, 2095, 2196], "roadmap": [10, 11], "parti": [10, 50, 2095, 2097, 2104, 2133, 2137, 2144, 2158, 2191], "triag": [10, 11], "meet": [10, 11, 13, 31, 49, 1013, 1018, 1345, 2133, 2194], "Their": [10, 1137, 2136, 2193], "articul": 10, "cohes": 10, "negoti": [10, 2165], "contenti": 10, "broad": [10, 2144, 2207], "stakehold": 10, "power": [10, 13, 33, 49, 1108, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1188, 1339, 1369, 1383, 1549, 1550, 1551, 1709, 1710, 1711, 1790, 1824, 1839, 1876, 1892, 1916, 2042, 2101, 2133, 2170], "veto": 10, "admin": 10, "amongst": 10, "commonli": [10, 38, 59, 1417, 1841, 2100, 2102, 2121, 2129, 2130, 2139, 2157, 2160, 2173, 2182, 2187, 2190], "merit": 10, "demonstr": [10, 35, 55, 68, 70, 78, 79, 1501, 2097, 2144, 2149, 2152, 2153, 2157, 2165, 2172, 2184, 2192, 2194, 2196, 2201], "expertis": 10, "align": [10, 25, 55, 770, 789, 795, 838, 850, 1001, 1145, 1269, 1371, 1493, 1501, 1517, 1533, 1547, 1552, 1576, 1577, 1598, 1635, 1643, 1644, 1645, 1671, 1672, 1699, 1700, 1740, 1759, 1789, 1830, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1866, 2021, 2119, 2126, 2130, 2141, 2149, 2155, 2163, 2171], "continu": [10, 24, 29, 31, 38, 54, 57, 59, 68, 401, 607, 836, 938, 1269, 1351, 1352, 1379, 1500, 1501, 1772, 1877, 2101, 2107, 2130, 2139, 2149, 2159, 2172, 2179, 2189, 2191, 2192, 2194, 2203], "light": [10, 2175, 2203], "mainten": [10, 50, 51], "emeritu": [10, 11], "inact": [10, 1101, 2133, 2206], "contact": [10, 29], "item": [10, 24, 31, 55, 70, 80, 81, 582, 583, 584, 588, 804, 805, 806, 828, 829, 830, 831, 968, 1014, 1517, 1583, 1592, 1795, 1943, 2095, 2097, 2098, 2099, 2100, 2108, 2119, 2121, 2133, 2140, 2149, 2155, 2156, 2157, 2158, 2165, 2175, 2176, 2188, 2190, 2191, 2193, 2198, 2203], "nomine": 10, "breadth": [10, 35], "testimoni": 10, "posit": [10, 24, 31, 35, 36, 38, 55, 68, 86, 401, 470, 544, 745, 886, 890, 892, 970, 972, 979, 992, 993, 994, 1044, 1161, 1162, 1163, 1164, 1165, 1173, 1175, 1179, 1181, 1199, 1209, 1210, 1213, 1227, 1229, 1273, 1274, 1307, 1310, 1315, 1318, 1345, 1346, 1357, 1359, 1370, 1373, 1374, 1388, 1408, 1423, 1472, 1477, 1478, 1495, 1509, 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2098, 2119, 2121, 2128, 2130, 2136, 2137, 2155, 2156, 2164, 2170, 2171, 2190, 2194, 2198], "interact": [10, 16, 24, 29, 36, 68, 891, 944, 1040, 1075, 1079, 2069, 2101, 2104, 2130, 2136, 2158, 2160, 2175, 2188, 2191, 2194, 2197, 2206], "final": [10, 20, 29, 31, 34, 38, 40, 50, 55, 56, 57, 59, 63, 64, 696, 699, 700, 770, 969, 987, 1000, 1145, 1184, 1203, 1242, 1269, 1387, 1405, 1410, 1502, 1533, 1535, 1552, 1598, 1611, 1762, 1841, 1974, 2021, 2097, 2099, 2100, 2101, 2119, 2136, 2139, 2141, 2144, 2147, 2149, 2151, 2152, 2155, 2157, 2158, 2166, 2167, 2188, 2190, 2192, 2203, 2204], "declin": 10, "conflict": [10, 25, 31, 51, 2158], "lack": [10, 12, 35, 968, 1351, 1352, 1379], "unfit": 10, "conduct": [10, 1772, 1885, 1998, 2165, 2184, 2188], "filial": 10, "romant": 10, "strength": 10, "candid": [10, 961, 2158], "letter": [10, 1145], "befit": 10, "candidaci": 10, "behind": [10, 29, 55, 2096, 2149, 2166, 2193], "75": [10, 1277, 1570, 1688, 1706, 1839, 1896, 2098, 2171], "unforeseen": 10, "circumst": [10, 50, 1001, 1239, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 2133], "perman": [10, 68, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 2130, 2167], "unavail": [10, 1056, 2099], "rank": [10, 24, 25, 29, 30, 31, 34, 35, 36, 37, 38, 40, 44, 48, 50, 51, 54, 56, 59, 70, 79, 1361, 1362, 1363, 1370, 1405, 1574, 1622, 1772, 1795, 1885, 1998, 2098, 2135, 2139, 2146, 2155, 2165, 2166, 2183, 2190, 2203, 2204, 2208], "elect": 10, "invit": [10, 2095], "convinc": 10, "approach": [10, 25, 29, 33, 68, 989, 1214, 1495, 1880, 1939, 1971, 2048, 2097, 2118, 2133, 2136, 2141, 2151, 2152, 2153, 2157, 2160, 2165, 2194, 2203], "interview": 10, "talk": [10, 53, 2104, 2143], "gather": [10, 29, 31, 33, 35, 36, 50, 59, 512, 2011, 2036, 2098, 2136, 2138, 2139, 2143, 2156, 2158, 2198], "read": [10, 13, 19, 20, 24, 29, 31, 36, 39, 40, 43, 50, 56, 59, 63, 68, 447, 955, 1018, 1103, 1128, 1196, 1198, 1199, 1231, 1323, 1387, 1877, 2104, 2119, 2120, 2126, 2130, 2131, 2133, 2135, 2137, 2143, 2147, 2151, 2158, 2160, 2165, 2188, 2191, 2192, 2194, 2196, 2203], "attend": [10, 745, 1588, 1626], "confer": [10, 1535], "pipelin": [10, 31, 68, 2096, 2165, 2194], "world": [10, 29, 33, 34, 40, 48, 50, 51, 55, 59, 1622, 1772, 2130, 2158, 2160, 2190, 2191, 2192, 2203], "cover": [10, 56, 57, 60, 68, 70, 1119, 1145, 1575, 1713, 1714, 1715, 2100, 2102, 2120, 2130, 2136, 2139, 2141, 2143, 2144, 2165, 2192, 2197, 2201, 2207], "push": [10, 42, 1105, 1107, 1214, 1381, 2048, 2149, 2159, 2191, 2203], "codeown": 10, "notifi": [10, 30, 51, 2167], "expert": [10, 33, 2157], "strongli": [10, 29, 40, 51, 57, 1520, 1521, 1522, 1526, 1993, 2095], "failur": [10, 29, 38, 40, 42, 43, 47, 48, 49, 50, 54, 55, 680, 948, 949, 1001, 1003, 1203, 1229, 1331, 1332, 1439, 2100, 2106, 2115, 2118, 2139, 2165, 2167, 2177, 2191, 2194, 2203], "revert": [10, 38, 59, 1619, 1748, 2167], "substanti": [10, 25, 2133, 2183, 2188, 2203], "syntact": [10, 43, 68], "incompat": [10, 15, 64, 982, 1312, 1781, 1782, 2131, 2158, 2188, 2203], "establish": [10, 20, 29, 50, 1877, 2121, 2130], "seri": [10, 35, 68, 1501, 1544, 2140, 2148, 2155, 2188, 2199, 2205], "lf": 10, "llc": 10, "guidelin": [10, 1806, 2146, 2151, 2158, 2160, 2161], "trademark": 10, "www": [10, 1501, 2175], "lfproject": 10, "acknowledg": [10, 29, 2167, 2170], "copyright": [10, 2185, 2204], "holder": 10, "independ": [10, 24, 29, 50, 55, 59, 67, 152, 153, 789, 795, 937, 938, 939, 940, 941, 942, 953, 1044, 1269, 1278, 1374, 1519, 1520, 1521, 1522, 1526, 1674, 1675, 1676, 1681, 1698, 1699, 1759, 1791, 1827, 1895, 2054, 2097, 2129, 2130, 2133, 2149, 2158, 2172, 2201, 2203], "authorship": 10, "claus": [10, 2138], "bsd": 10, "licens": 10, "opensourc": 10, "outbound": 10, "inbound": 10, "q": [10, 25, 38, 430, 478, 768, 813, 1259, 1336, 1352, 1355, 1374, 1416, 1475, 1547, 1588, 1642, 1789, 1883, 1885, 1895, 1896, 1998, 2097, 2098, 2126, 2141, 2148, 2155, 2170, 2171, 2193], "partli": [10, 2100], "domain": [10, 38, 43, 694, 850, 853, 914, 1159, 1160, 1161, 1162, 1164, 1165, 1166, 1167, 1168, 1173, 1175, 1176, 1177, 1178, 1269, 2121, 2155, 2160], "absolut": [10, 12, 15, 68, 91, 681, 704, 948, 949, 1193, 1304, 1344, 1349, 1370, 1373, 1375, 1396, 1542, 1548, 1614, 1697, 1701, 1744, 1800, 1801, 1803, 1804, 1806, 1809, 1810, 1811, 1812, 1830, 1838, 1889, 1915, 1946, 2098, 2108, 2144, 2155, 2156, 2171, 2176, 2177, 2184], "health": 10, "success": [10, 31, 38, 40, 47, 68, 285, 1346, 1357, 1359, 1405, 1802, 1979, 1980, 1981, 1983, 1984, 2133, 2144, 2155, 2170, 2204], "am": 10, "grant": 10, "purchas": 10, "board": 10, "driven": [10, 2155], "clearli": [10, 1229, 2107, 2158], "sponsorship": 10, "foundat": 10, "ptf": 10, "minor": [10, 1068, 1346, 1362, 1453, 2170, 2191], "committ": 10, "prior": [10, 20, 25, 29, 31, 40, 929, 933, 934, 935, 1006, 1388, 1628, 1630, 1740, 1806, 2126, 2131, 2133, 2136, 2157, 2160, 2188, 2194], "walkthrough": [10, 2129], "facebook": 10, "infrastructur": [10, 35, 42, 2158, 2190], "employe": 10, "expand": [10, 38, 254, 492, 907, 929, 933, 934, 935, 948, 949, 984, 1368, 1369, 1417, 1588, 1633, 1733, 2031, 2095, 2098, 2106, 2119, 2120, 2131, 2133, 2136, 2137, 2155, 2156, 2158, 2174, 2198], "deliv": [10, 2159], "offici": [10, 29, 1486, 2140, 2160], "showcas": [10, 13, 37, 1578, 2133, 2146], "whenev": [10, 36, 55, 57, 909, 1789, 1790, 2113, 2118, 2121, 2136, 2163, 2166, 2167, 2193, 2203, 2204, 2205], "fix": [11, 19, 24, 29, 38, 40, 50, 55, 57, 61, 64, 68, 261, 807, 815, 957, 1203, 1329, 1361, 1524, 1525, 1601, 1679, 1680, 1699, 1772, 1881, 2097, 2098, 2113, 2128, 2129, 2133, 2138, 2139, 2146, 2150, 2152, 2155, 2156, 2157, 2188, 2191, 2194, 2201, 2203, 2204], "plu": [11, 15, 31, 937, 1199, 1362, 2139, 2170], "quarterli": 11, "chintala": 11, "edward": 11, "yang": [11, 1388], "ezyang": [11, 2104, 2174, 2203], "greg": 11, "chanan": 11, "gchanan": 11, "dmytro": 11, "dzhulgakov": 11, "nikita": 11, "shulga": 11, "malfet": 11, "alban": 11, "desmaison": 11, "alband": 11, "piotr": 11, "bialecki": 11, "ptrblck": 11, "mikayla": 11, "gawarecki": 11, "mikaylagawarecki": 11, "joel": [11, 1885, 1998], "schlosser": 11, "jbschlosser": 11, "sam": 11, "gross": 11, "colesburi": 11, "adam": [11, 30, 31, 34, 38, 59, 957, 1844, 1845, 1847, 1859, 1863, 2157, 2203], "paszk": 11, "apaszk": 11, "jane": 11, "xu": [11, 59], "janeyx99": 11, "ilqar": 11, "ramazanli": 11, "iramazanli": 11, "vincent": 11, "quennevil": 11, "belair": 11, "vincentqb": 11, "jeffrei": 11, "wan": 11, "soulitz": 11, "animesh": 11, "jain": 11, "anijain2305": 11, "jason": [11, 2192], "ansel": [11, 2192], "jansel": 11, "elia": 11, "ellison": 11, "eellison": 11, "horac": 11, "he": [11, 2128], "chille": 11, "shunt": 11, "zhang": 11, "shunting314": [11, 2197], "jiong": 11, "gong": 11, "jgong5": 11, "brian": 11, "hirsh": 11, "bdhirsh": [11, 2039], "richard": [11, 38], "zou": 11, "zou3519": 11, "avik": 11, "chaudhuri": 11, "avikchaudhuri": 11, "yanan": 11, "cao": 11, "gmagogsfm": 11, "bin": [11, 40, 48, 50, 299, 300, 836, 972, 1276, 1277, 1278, 2098, 2100, 2156, 2175, 2200], "bao": [11, 2200], "desertfir": 11, "angela": 11, "yi": 11, "angelayi": [11, 57], "chen": 11, "chenyang78": 11, "michael": [11, 2204], "suo": 11, "jame": 11, "reed": 11, "jamesr66a": 11, "zach": 11, "devito": 11, "zdevito": 11, "fritz": 11, "obermey": 11, "fritzo": 11, "neeraj": 11, "pradhan": 11, "neerajprad": 11, "alican": 11, "bozkurt": 11, "alicanb": 11, "vishwak": 11, "srinivasan": 11, "vishwakftw": 11, "Will": [11, 29, 35, 59, 68, 1003, 1227, 1982, 2099, 2120, 2155, 2188, 2199], "constabl": [11, 2199], "wconstab": 11, "howard": 11, "huang": 11, "wanchao": 11, "liang": 11, "wanchaol": 11, "ke": 11, "wen": 11, "kwen2501": 11, "chien": 11, "chin": 11, "fegin": 11, "tristan": 11, "rice": 11, "d4l3k": 11, "shen": 11, "li": [11, 13, 1475, 1896, 2133, 2191, 2194], "mrshenli": 11, "pritam": 11, "damania": 11, "pritamdamania87": 11, "yanli": 11, "zhao": 11, "zhaojuanmao": 11, "rohan": 11, "varma": 11, "junji": 11, "wang": [11, 38], "fduwjj": 11, "alisson": 11, "azzolini": 11, "aazzolini": 11, "kiuk": 11, "chung": 11, "kiukchung": 11, "pieter": 11, "noordhui": 11, "pietern": 11, "mingzh": 11, "mingzhe09088": 11, "omkar": 11, "salpekar": 11, "osalpekar": 11, "simon": 11, "ssnl": 11, "vitali": 11, "fedyunin": 11, "vitalyfedyunin": 11, "mario": 11, "lezcano": 11, "mike": 11, "ruberri": 11, "mruberri": 11, "ivan": 11, "yashchuk": 11, "ivanyashchuk": 11, "pearu": 11, "peterson": 11, "vedeneev": 11, "nikitav": 11, "christian": 11, "puhrsch": 11, "cpuhrsch": 11, "andrew": [11, 1388, 1880], "amjam": 11, "driss": 11, "guessou": 11, "drisspg": 11, "natalia": 11, "gimelshein": 11, "ngimel": 11, "georg": 11, "qi": 11, "peter": 11, "bell": 11, "peterbell10": 11, "xiaob": 11, "xiaobingsup": 11, "mingfei": 11, "ma": 11, "mingfeima": 11, "xiaoqiang": 11, "zheng": 11, "xq": 11, "ilia": 11, "cherniavskii": 11, "cher": 11, "bai": 11, "bddppq": 11, "yinghai": 11, "jianhui": 11, "sarofeen": 11, "csarofeen": 11, 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"hdr": 11, "fang": 11, "houseroad": 11, "negin": 11, "raoof": 11, "neginraoof": 11, "spandan": 11, "tiwari": 11, "spandantiwari": 11, "david": [11, 1486], "reiss": 11, "dreiss": 11, "raziel": 11, "guevara": 11, "linbin": 11, "yu": 11, "linbinyu": 11, "kobzarev": 11, "ivankobzarev": 11, "tao": 11, "xta0": 11, "mark": [11, 31, 35, 50, 67, 68, 70, 75, 79, 485, 850, 929, 930, 931, 934, 957, 1004, 1006, 1223, 1434, 1435, 1772, 1837, 2097, 2099, 2130, 2135, 2136, 2144, 2149, 2158, 2159, 2166, 2176, 2182, 2188, 2191, 2194, 2195], "saroufim": [11, 2194], "msaroufim": 11, "vasilii": 11, "kuznetsov": 11, "vkuzo": 11, "jerri": 11, "jerryzh168": [11, 805], "zafar": 11, "takhirov": 11, "raghuraman": 11, "krishnamoorthi": 11, "raghuramank100": 11, "guoliang": 11, "hua": 11, "nbcsm": 11, "teng": 11, "gao": 11, "gaoteng": 11, "git": [11, 2185, 2194, 2204], "johnson": 11, "peterjc123": [11, 2150], "kulin": 11, "seth": 11, "kulinseth": 11, "ramin": 11, "azarmehr": 11, "razarmehr": 11, "alfredo": 11, 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1241, 1242, 1243, 1254, 1313, 1316, 1827, 1993, 2101, 2110, 2121, 2130, 2136, 2160, 2190, 2191, 2194, 2204, 2205], "bj": 12, "j": [12, 29, 38, 312, 314, 320, 512, 514, 516, 702, 927, 937, 939, 1015, 1145, 1161, 1162, 1169, 1175, 1179, 1180, 1256, 1312, 1315, 1351, 1352, 1363, 1379, 1388, 1395, 1403, 1405, 1410, 1520, 1521, 1522, 1526, 1582, 1585, 1674, 1675, 1676, 1681, 1880, 1889, 1896, 1975, 1993, 1997, 2045, 2099, 2128, 2130, 2136, 2141, 2149, 2175], "imaginari": [12, 310, 948, 1015, 1022, 1166, 1168, 1176, 1177, 1178, 1286, 1305, 1307, 1308, 1311, 1908, 1993, 2046, 2047, 2101, 2130, 2141, 2177], "satisfi": [12, 22, 25, 38, 56, 57, 68, 616, 704, 770, 806, 938, 948, 949, 985, 1161, 1162, 1164, 1166, 1168, 1176, 1177, 1178, 1193, 1228, 1329, 1355, 1381, 1382, 1486, 1501, 1528, 1533, 1552, 1598, 1806, 1823, 1915, 1931, 1971, 1993, 2100, 2104, 2130, 2133, 2139, 2141, 2157, 2159, 2170, 2173, 2188, 2192], "equat": [12, 994, 1145, 1222, 1233, 1259, 1273, 1359, 1360, 1361, 1362, 1363, 1365, 1376, 1378, 1494, 1956, 1977, 2023, 2098, 2130, 2141, 2157, 2171], "frequent": [12, 31, 1486, 2096, 2115, 2133, 2146, 2147, 2149, 2170, 2182, 2184, 2188, 2196, 2203], "occur": [12, 24, 29, 35, 43, 44, 50, 59, 64, 195, 314, 924, 957, 1001, 1104, 1145, 1229, 1312, 1318, 1329, 1359, 1501, 1622, 1641, 1672, 1830, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 2099, 2113, 2115, 2120, 2121, 2129, 2130, 2133, 2138, 2147, 2155, 2159, 2165, 2167, 2174, 2177, 2190, 2191, 2194, 2196, 2199, 2201, 2203, 2204, 2205, 2206], "mathemat": [12, 68, 937, 938, 939, 940, 941, 942, 1193, 1269, 1494, 1496, 1497, 1498, 1544, 1545, 1546, 1547, 1622, 1707, 1747, 1772, 1830, 1915, 1973, 1974, 2100, 2104, 2130, 2147, 2171, 2179], "topic": [12, 55, 2143, 2144, 2192], "tradition": [12, 2121], "torchaudio": [12, 2096, 2140], "mimick": 12, "assembli": 12, "lapack": [12, 1259, 1346, 1357, 1358, 1359, 1361, 1364, 1377, 1895, 1997], "spectral": [12, 1335, 1351, 1379, 1790, 1814, 1824, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2147], "fft": [12, 2096, 2133], "4621": 12, "0303j": 12, "2438": [12, 1361, 1679], "5874j": 12, "7706": 12, "1421j": 12, "2110": 12, "1918j": 12, "complex128": [12, 174, 1015, 1188, 1198, 1294, 1315, 1345, 1346, 1351, 1352, 1353, 1354, 1355, 1356, 1509, 1510, 1511, 1582, 1663, 1664, 1665, 1836, 1880, 1889, 1936, 2172, 2173, 2176, 2177, 2194], "complex64": [12, 177, 993, 994, 1015, 1162, 1164, 1165, 1170, 1171, 1198, 1294, 1347, 1370, 1373, 1509, 1510, 1511, 1663, 1664, 1665, 1836, 1889, 1936, 2172, 2173, 2176, 2177, 2194], "apart": [12, 2100, 2130], "linspac": [12, 972, 996, 1166, 1176, 1417, 2098, 2102, 2156], "logspac": [12, 2098, 2102, 2156], "arang": [12, 24, 29, 39, 512, 606, 701, 702, 703, 707, 709, 915, 916, 917, 983, 995, 999, 1128, 1143, 1161, 1165, 1173, 1179, 1181, 1185, 1186, 1187, 1188, 1195, 1196, 1279, 1336, 1337, 1341, 1344, 1368, 1371, 1372, 1385, 1478, 1502, 1503, 1504, 1604, 1605, 1606, 1607, 1608, 1635, 1636, 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1003, 1214, 1227, 2048, 2120, 2121, 2130, 2135, 2189, 2190, 2195, 2196], "real_param": 12, "p": [12, 25, 31, 38, 57, 59, 68, 153, 233, 285, 424, 425, 452, 487, 490, 491, 696, 699, 969, 971, 981, 989, 1139, 1336, 1347, 1353, 1354, 1362, 1363, 1405, 1407, 1410, 1420, 1471, 1490, 1519, 1520, 1521, 1522, 1524, 1525, 1526, 1547, 1549, 1550, 1551, 1587, 1591, 1593, 1631, 1632, 1654, 1673, 1674, 1675, 1676, 1679, 1680, 1681, 1709, 1710, 1711, 1721, 1725, 1728, 1729, 1740, 1756, 1778, 1787, 1793, 1795, 1801, 1810, 1830, 1862, 1916, 1973, 2098, 2099, 2129, 2130, 2133, 2138, 2141, 2146, 2157, 2159, 2170, 2171, 2197, 2198], "complex_optim": 12, "adamw": [12, 1843, 1847, 1859, 2157], "real_optim": 12, "slight": [12, 1935, 2170, 2173], "discrep": [12, 1772, 1902, 2141], "foreach": [12, 1777, 1778, 1779, 1780, 1787, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 2157], "v": [12, 31, 35, 47, 55, 63, 68, 486, 487, 543, 927, 938, 940, 941, 942, 968, 985, 1145, 1202, 1212, 1214, 1229, 1312, 1329, 1351, 1379, 1383, 1388, 1588, 1631, 1632, 1642, 1679, 1725, 1791, 1822, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1885, 1918, 1978, 1982, 1997, 1998, 2048, 2097, 2098, 2099, 2100, 2113, 2130, 2136, 2141, 2150, 2157, 2158, 2170, 2175, 2187], "forloop": 12, "numerical_accuraci": 12, "impact": [12, 25, 29, 57, 1013, 1101, 1939, 2110, 2113, 2121, 2130, 2160, 2174, 2194, 2199, 2203, 2204], "pointwis": [12, 38, 1001, 1207, 1208, 1393, 1394, 1547, 2119, 2121, 2131, 2193, 2196, 2197], "lbfg": [12, 2157], "yet": [12, 29, 30, 35, 36, 40, 55, 56, 59, 64, 67, 68, 77, 485, 852, 1039, 1119, 1240, 1318, 1320, 1333, 1421, 1772, 1797, 1801, 1803, 1843, 1844, 2096, 2099, 2100, 2106, 2112, 2120, 2121, 2133, 2149, 2155, 2156, 2160, 2165, 2167, 2170, 2171, 2173, 2188, 2191, 2193, 2194, 2199, 2201, 2203, 2204], "fulli": [12, 15, 24, 29, 31, 33, 34, 35, 37, 55, 63, 68, 680, 1108, 1227, 1315, 1578, 1579, 1580, 1582, 1880, 2100, 2106, 2112, 2136, 2158, 2160, 2190, 2191, 2198, 2203], "quantiz": [12, 28, 55, 68, 218, 325, 337, 471, 472, 473, 474, 475, 477, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 887, 888, 889, 890, 891, 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8, "discuss": 8, "submit": 8, "pull": 8, "request": 8, "open": 8, "review": 8, "code": [8, 68, 2097, 2133, 2140, 2158, 2185, 2194, 2203], "readabl": 8, "test": [8, 2100, 2104, 2136, 2158, 2177, 2200], "case": [8, 2097, 2172], "make": [8, 10, 2188, 2191], "codebas": 8, "more": [8, 55, 60, 2165, 2182], "robust": 8, "triag": 8, "about": [8, 2130, 2165, 2193], "sourc": [8, 2140, 2148, 2150, 2158], "develop": [8, 55, 2096, 2151, 2182], "common": [8, 29, 68, 2143, 2160], "mistak": 8, "To": 8, "avoid": [8, 2146, 2148, 2155, 2158], "frequent": [8, 2097, 2138, 2155, 2160, 2194], "ask": [8, 2097, 2138, 2155, 2160, 2194], "question": [8, 2097, 2138, 2155, 2160, 2194], "On": [8, 25, 2099], "python": [8, 9, 29, 55, 64, 71, 72, 73, 74, 75, 76, 77, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2104, 2136, 2148, 2155, 2184, 2191], "doc": [8, 11, 2121, 2142], "c": [8, 11, 16, 2104, 2130, 2142, 2149, 2155, 2184], "overview": [8, 19, 20, 55, 68, 2151, 2152, 2158, 2159, 2170, 2189, 2192], "design": [9, 2100, 2135, 2165, 2166, 2167], "philosophi": 9, "principl": [9, 10], "1": [9, 35, 51, 62, 2097, 2137, 2191], "usabl": 9, "perform": [9, 11, 2144, 2149, 2193, 2200, 2201, 2204], "2": [9, 35, 62, 2097, 2133, 2137, 2138, 2160, 2163, 2199, 2200, 2204], "simpl": [9, 2099, 2100, 2144, 2152, 2166, 2203], "easi": 9, "3": [9, 51, 62], "first": [9, 2158], "best": [9, 2133, 2146, 2160, 2187], "class": [9, 36, 43, 68, 2097, 2099, 2100, 2102, 2155, 2157, 2158, 2176], "languag": [9, 2096, 2097, 2099, 2100, 2101], "interoper": 9, "mechan": [10, 2121, 2127, 2130, 2141], "summari": [10, 2160, 2203], "modul": [10, 11, 57, 61, 63, 68, 1582, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 2097, 2098, 2099, 2100, 2102, 2122, 2130, 2136, 2144, 2149, 2158, 2160, 2199], "core": [10, 11, 2198], "lead": [10, 11], "bdfl": [10, 11], "nomin": [10, 2100], "confirm": 10, "remov": [10, 1813, 2119], "The": [10, 68, 2100, 2130, 2190], "re": [10, 2158], "scope": 10, "project": 10, "decis": 10, "uncontroversi": 10, "chang": 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"jit": [11, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1331, 1332, 1333, 1334, 2097, 2100, 2103], "torchscript": [11, 16, 2097, 2098, 2099, 2100, 2102, 2132, 2143, 2151, 2153, 2155, 2156, 2158], "deploi": [11, 27], "distribut": [11, 29, 31, 32, 33, 34, 35, 36, 37, 38, 2122, 2127, 2135, 2142, 2144, 2165, 2166, 2194], "rng": 11, "multiprocess": [11, 48, 2118, 2133, 2146, 2150], "linear": [11, 12, 742, 743, 766, 774, 791, 1210, 1569, 1705, 2122, 2127, 2147, 2170], "algebra": [11, 12, 2147, 2170], "linalg": [11, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 2105, 2147], "spars": [11, 1969, 1970, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 2122, 2127, 2170], "nestedtensor": 11, "nest": [11, 2121], "maskedtensor": [11, 2107], "mask": [11, 2107], "fast": 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"nn.Module.__call__ Hooks Usage and limitations": [[2199, "nn-module-call-hooks-usage-and-limitations"]], "state_dict Hooks": [[2199, "state-dict-hooks"]], "Dynamo Overview": [[2192, "dynamo-overview"]], "Dynamo Internals": [[2192, "dynamo-internals"]], "What is a guard?": [[2192, "what-is-a-guard"]], "What is Dynamo doing?": [[2192, "what-is-dynamo-doing"]], "How to inspect artifacts generated by Dynamo?": [[2192, "how-to-inspect-artifacts-generated-by-dynamo"]], "torch.utils": [[2210, "module-torch.utils"]], "Fake tensor": [[2193, "fake-tensor"]], "Related work": [[2193, "related-work"]], "API: the important bits": [[2193, "api-the-important-bits"]], "Details": [[2193, "details"], [2126, "details"]], "About the tensor subclass": [[2193, "about-the-tensor-subclass"]], "How is each individual operator implemented?": [[2193, "how-is-each-individual-operator-implemented"]], "How does the converter work?": [[2193, "how-does-the-converter-work"]], "Performance characteristics": [[2193, "performance-characteristics"]], "Fake tensor of fake tensor?": [[2193, "fake-tensor-of-fake-tensor"]], "Interaction with dynamic shapes": [[2193, "interaction-with-dynamic-shapes"]], "Other resources": [[2193, "other-resources"]], "Frequently Asked Questions": [[2194, "frequently-asked-questions"], [2097, "frequently-asked-questions"], [2138, "frequently-asked-questions"], [2160, "frequently-asked-questions"], [2155, "frequently-asked-questions"], [8, "frequently-asked-questions"]], "Does torch.compile support training?": [[2194, "does-torch-compile-support-training"]], "Do you support Distributed code?": [[2194, "do-you-support-distributed-code"]], "Do I still need to export whole graphs?": [[2194, "do-i-still-need-to-export-whole-graphs"]], "Why is my code crashing?": [[2194, "why-is-my-code-crashing"]], "Why is compilation slow?": [[2194, "why-is-compilation-slow"]], "Why are you recompiling in production?": [[2194, "why-are-you-recompiling-in-production"]], "How are you speeding up my code?": [[2194, "how-are-you-speeding-up-my-code"]], "Why am I not seeing speedups?": [[2194, "why-am-i-not-seeing-speedups"]], "Graph Breaks": [[2194, "graph-breaks"], [2204, "graph-breaks"], [55, "graph-breaks"]], "Identifying the cause of a graph break": [[2194, "identifying-the-cause-of-a-graph-break"]], "Why didn\u2019t my code recompile when I changed it?": [[2194, "why-didnt-my-code-recompile-when-i-changed-it"]], "Why am I getting incorrect results?": [[2194, "why-am-i-getting-incorrect-results"]], "Why am I getting OOMs?": [[2194, "why-am-i-getting-ooms"]], "Does torch.func work with torch.compile (for grad and vmap transforms)?": [[2194, "does-torch-func-work-with-torch-compile-for-grad-and-vmap-transforms"]], "Calling torch.func transform inside of a function handled with torch.compile": [[2194, "calling-torch-func-transform-inside-of-a-function-handled-with-torch-compile"]], "Compiling torch.func.grad with torch.compile": [[2194, "compiling-torch-func-grad-with-torch-compile"]], "Compiling torch.vmap with torch.compile": [[2194, "compiling-torch-vmap-with-torch-compile"]], "Compiling functions besides the ones which are supported (escape hatch)": [[2194, "compiling-functions-besides-the-ones-which-are-supported-escape-hatch"]], "Does NumPy work with torch.compile?": [[2194, "does-numpy-work-with-torch-compile"]], "Which NumPy features does torch.compile support?": [[2194, "which-numpy-features-does-torch-compile-support"]], "Can I compile NumPy code using torch.compile?": [[2194, "can-i-compile-numpy-code-using-torch-compile"]], "Can I execute NumPy code on CUDA and compute gradients via torch.compile?": [[2194, "can-i-execute-numpy-code-on-cuda-and-compute-gradients-via-torch-compile"]], "How do I debug NumPy code under torch.compile?": [[2194, "how-do-i-debug-numpy-code-under-torch-compile"]], "I torch.compile some NumPy code and I did not see any speed-up.": [[2194, "i-torch-compile-some-numpy-code-and-i-did-not-see-any-speed-up"]], "Which API to use for fine grain tracing?": [[2194, "which-api-to-use-for-fine-grain-tracing"]], "How do I graph break on a function?": [[2194, "how-do-i-graph-break-on-a-function"]], "What\u2019s the difference between torch._dynamo.disable and torch._dynamo.disallow_in_graph": [[2194, "what-s-the-difference-between-torch-dynamo-disable-and-torch-dynamo-disallow-in-graph"]], "What\u2019s the difference between torch._dynamo.disable and torch._dynamo_skip": [[2194, "what-s-the-difference-between-torch-dynamo-disable-and-torch-dynamo-skip"]], "torch.overrides": [[2205, "module-torch.overrides"]], "Functions": [[2205, "functions"], [2099, "functions"], [2097, "functions"], [2171, "functions"], [2155, "functions"]], "PyTorch 2.0 Performance Dashboard": [[2200, "pytorch-2-0-performance-dashboard"]], "How to read the dashboard?": [[2200, "how-to-read-the-dashboard"]], "What is measured on the dashboard?": [[2200, "what-is-measured-on-the-dashboard"]], "Can I check if my PR affects TorchInductor\u2019s performance on the dashboard before merging?": [[2200, "can-i-check-if-my-pr-affects-torchinductor-s-performance-on-the-dashboard-before-merging"]], "How can I run any performance test locally?": [[2200, "how-can-i-run-any-performance-test-locally"]], "Torch Environment Variables": [[2207, "torch-environment-variables"]], "AOTInductor Minifier": [[2185, "aotinductor-minifier"]], "Example Code": [[2185, "example-code"]], "Minifier Launcher": [[2185, "minifier-launcher"]], "Minified Result": [[2185, "minified-result"]], "Getting Started": [[2196, "getting-started"], [8, "getting-started"]], "Using a pretrained model": [[2196, "using-a-pretrained-model"]], "Next Steps": [[2196, "next-steps"]], "Type Info": [[2209, "type-info"]], "torch.finfo": [[2209, "torch-finfo"]], "torch.iinfo": [[2209, "torch-iinfo"]], "Profiling to understand torch.compile performance": [[2201, "profiling-to-understand-torch-compile-performance"]], "What to use torch.profiler for:": [[2201, "what-to-use-torch-profiler-for"]], "Basics of using torch.profiler and viewing traces": [[2201, "basics-of-using-torch-profiler-and-viewing-traces"]], "Working around CUDA Graph profiling issues": [[2201, "working-around-cuda-graph-profiling-issues"]], "Understanding compilation time": [[2201, "understanding-compilation-time"]], "Finding graph breaks: \u201cTorch-Compiled Region\u201d and \u201cCompiledFunction\u201d": [[2201, "finding-graph-breaks-torch-compiled-region-and-compiledfunction"]], "Operator Kernels": [[2201, "operator-kernels"]], "Launch overhead": [[2201, "launch-overhead"]], "Custom Backends": [[2189, "custom-backends"]], "Overview": [[2189, "overview"], [68, "module-torch.fx"], [55, "overview"], [2152, "overview"], [2159, "module-torch.profiler"], [2151, "overview"], [20, "module-torch.cuda.tunable"], [19, "module-torch.cuda._sanitizer"]], "Registering Custom Backends": [[2189, "registering-custom-backends"]], "Custom Backends after AOTAutograd": [[2189, "custom-backends-after-aotautograd"]], "Examples": [[2189, "examples"], [2140, "examples"], [13, "examples"], [45, "examples"]], "Debugging Backend": [[2189, "debugging-backend"]], "Speedy Backend": [[2189, "speedy-backend"]], "Composable Backends": [[2189, "composable-backends"]], "Best Practices for Backends": [[2187, "best-practices-for-backends"]], "x86 CPU": [[2187, "x86-cpu"], [11, "x86-cpu"]], "Dynamo Deep-Dive": [[2191, "dynamo-deep-dive"]], "A Gentle Introduction to Dynamo": [[2191, "a-gentle-introduction-to-dynamo"]], "PEP 523: Adding a frame evaluation API to CPython": [[2191, "pep-523-adding-a-frame-evaluation-api-to-cpython"]], "Implementing CPython in Python": [[2191, "implementing-cpython-in-python"]], "Generating the Output Graph": [[2191, "generating-the-output-graph"]], "Making Dynamo Sound: Guards": [[2191, "making-dynamo-sound-guards"]], "Symbolic Shapes": [[2191, "symbolic-shapes"]], "Static by default": [[2191, "static-by-default"]], "0, 1 are always specialized": [[2191, "are-always-specialized"]], "Duck shaping": [[2191, "duck-shaping"]], "Guards on symbolic ints": [[2191, "guards-on-symbolic-ints"]], "Making Dynamo Complete: Graph Breaks": [[2191, "making-dynamo-complete-graph-breaks"]], "Conclusion": [[2191, "conclusion"]], "Footnotes": [[2191, "footnotes"]], "torch.xpu": [[2211, "module-torch.xpu"]], "Random Number Generator": [[2211, "random-number-generator"], [18, "random-number-generator"]], "Streams and events": [[2211, "streams-and-events"], [2116, "streams-and-events"], [17, "streams-and-events"], [18, "streams-and-events"]], "Memory management": [[2211, "memory-management"], [2142, "memory-management"], [2133, "memory-management"], [18, "memory-management"]], "torch.compile Troubleshooting": [[2203, "torch-compile-troubleshooting"]], "Setting Expectations": [[2203, "setting-expectations"]], "Compile times": [[2203, "compile-times"]], "Terminology": [[2203, "terminology"], [2100, "terminology"]], "Graph break": [[2203, "graph-break"]], "Guards": [[2203, "guards"]], "Recompilation": [[2203, "recompilation"]], "Dynamic Shapes": [[2203, "dynamic-shapes"]], "Logging Tools": [[2203, "logging-tools"]], "tlparse / TORCH_TRACE": [[2203, "tlparse-torch-trace"]], "TORCH_LOGS": [[2203, "torch-logs"]], "tlparse vs. TORCH_LOGS": [[2203, "tlparse-vs-torch-logs"]], "Simple Workarounds": [[2203, "simple-workarounds"]], "Where to apply torch.compile?": [[2203, "where-to-apply-torch-compile"]], "Disabling and Suppressing Errors": [[2203, "disabling-and-suppressing-errors"]], "Resolving graph breaks": [[2203, "resolving-graph-breaks"]], "Data-dependent operations": [[2203, "data-dependent-operations"]], "Custom ops": [[2203, "custom-ops"]], "Printing": [[2203, "printing"]], "Incorrect code": [[2203, "incorrect-code"]], "Dealing with recompilations": [[2203, "dealing-with-recompilations"]], "Is dynamic shapes enabled?": [[2203, "is-dynamic-shapes-enabled"]], "Changing the cache size limit": [[2203, "changing-the-cache-size-limit"]], "Wrapping constants with tensors": [[2203, "wrapping-constants-with-tensors"]], "Reporting Issues": [[2203, "reporting-issues"], [8, "reporting-issues"]], "Ablation": [[2203, "ablation"]], "Bisecting": [[2203, "bisecting"]], "Creating a reproducer": [[2203, "creating-a-reproducer"]], "Minifier": [[2203, "minifier"]], "Debugging Deeper": [[2203, "debugging-deeper"]], "TorchDynamo": [[2203, "torchdynamo"], [11, "torchdynamo"]], "Logging what Dynamo is tracing": [[2203, "logging-what-dynamo-is-tracing"]], "Breakpointing Dynamo tracing": [[2203, "breakpointing-dynamo-tracing"]], "Bytecode generation errors": [[2203, "bytecode-generation-errors"]], "AOTAutograd": [[2203, "aotautograd"]], "Summary of TORCH_LOGS options": [[2203, "summary-of-torch-logs-options"]], "Related Articles": [[2203, "related-articles"]], "PyTorch 2.0 Troubleshooting (old)": [[2204, "pytorch-2-0-troubleshooting-old"]], "Title": [[2204, "id1"]], "Diagnosing Runtime Errors": [[2204, "diagnosing-runtime-errors"]], "Torchdynamo Errors": [[2204, "torchdynamo-errors"]], "Diagnosing TorchInductor Errors": [[2204, "diagnosing-torchinductor-errors"]], "Minifying TorchInductor Errors": [[2204, "minifying-torchinductor-errors"]], "Minifying Backend Compiler Errors": [[2204, "minifying-backend-compiler-errors"]], "Performance Profiling": [[2204, "performance-profiling"]], "Accessing TorchDynamo Profiler": [[2204, "accessing-torchdynamo-profiler"]], "TorchInductor Debugging using TORCH_COMPILE_DEBUG": [[2204, "torchinductor-debugging-using-torch-compile-debug"]], "Identifying the Cause of a Graph Break": [[2204, "identifying-the-cause-of-a-graph-break"]], "Excessive Recompilation": [[2204, "excessive-recompilation"]], "Accuracy Debugging": [[2204, "accuracy-debugging"]], "Extended Debugging": [[2204, "extended-debugging"]], "Cold Start Timing and Cache Corruption Debugging": [[2204, "cold-start-timing-and-cache-corruption-debugging"]], "Understanding CUDA Memory Usage": [[2206, "understanding-cuda-memory-usage"]], "Generating a Snapshot": [[2206, "generating-a-snapshot"]], "Using the visualizer": [[2206, "using-the-visualizer"]], "Active Memory Timeline": [[2206, "active-memory-timeline"]], "Allocator State History": [[2206, "allocator-state-history"]], "Snapshot API Reference": [[2206, "snapshot-api-reference"]], "PYTORCH ProcessGroupNCCL Environment Variables": [[2208, "pytorch-processgroupnccl-environment-variables"]], "TorchDynamo APIs for fine-grained tracing": [[2195, "torchdynamo-apis-for-fine-grained-tracing"]], "TorchDynamo APIs to control fine-grained tracing": [[2195, "id1"]], "torch.compiler.disable": [[2195, "torch-compiler-disable"], [1007, "torch-compiler-disable"]], "torch._dynamo.disallow_in_graph": [[2195, "torch-dynamo-disallow-in-graph"]], "torch.compiler.allow_in_graph": [[2195, "torch-compiler-allow-in-graph"], [1003, "torch-compiler-allow-in-graph"]], "torch.nn.functional.pairwise_distance": [[1728, "torch-nn-functional-pairwise-distance"]], "torch.nn.functional.selu": [[1741, "torch-nn-functional-selu"]], "torch.nn.functional.relu": [[1734, "torch-nn-functional-relu"]], "torch.nn.functional.softplus": [[1748, "torch-nn-functional-softplus"]], "torch.nn.functional.rrelu": [[1738, "torch-nn-functional-rrelu"]], "torch.nn.functional.silu": [[1743, "torch-nn-functional-silu"]], "torch.nn.functional.rms_norm": [[1737, "torch-nn-functional-rms-norm"]], "torch.nn.functional.pixel_shuffle": [[1730, "torch-nn-functional-pixel-shuffle"]], "torch.nn.functional.upsample_nearest": [[1761, "torch-nn-functional-upsample-nearest"]], "torch.nn.functional.softshrink": [[1749, "torch-nn-functional-softshrink"]], "torch.nn.functional.torch.nn.parallel.data_parallel": [[1755, "torch-nn-functional-torch-nn-parallel-data-parallel"]], "torch.nn.modules.module.register_module_full_backward_hook": [[1767, "torch-nn-modules-module-register-module-full-backward-hook"]], "torch.nn.functional.triplet_margin_with_distance_loss": [[1757, "torch-nn-functional-triplet-margin-with-distance-loss"]], "torch.nn.modules.module.register_module_backward_hook": [[1763, "torch-nn-modules-module-register-module-backward-hook"]], "torch.nn.modules.module.register_module_forward_hook": [[1765, "torch-nn-modules-module-register-module-forward-hook"]], "torch.nn.functional.multilabel_margin_loss": [[1722, "torch-nn-functional-multilabel-margin-loss"]], "torch.nn.functional.softsign": [[1750, "torch-nn-functional-softsign"]], "torch.nn.functional.max_unpool1d": [[1716, "torch-nn-functional-max-unpool1d"]], "torch.nn.functional.normalize": [[1725, "torch-nn-functional-normalize"]], "LazyModuleMixin": [[1762, "lazymodulemixin"]], "torch.nn.functional.soft_margin_loss": [[1745, "torch-nn-functional-soft-margin-loss"]], "torch.nn.modules.module.register_module_forward_pre_hook": [[1766, "torch-nn-modules-module-register-module-forward-pre-hook"]], "torch.nn.functional.pad": [[1727, "torch-nn-functional-pad"]], "torch.nn.functional.poisson_nll_loss": [[1732, "torch-nn-functional-poisson-nll-loss"]], "torch.nn.functional.multi_margin_loss": [[1721, "torch-nn-functional-multi-margin-loss"]], "torch.nn.functional.tanh": [[1751, "torch-nn-functional-tanh"]], "torch.nn.functional.sigmoid": [[1742, "torch-nn-functional-sigmoid"]], "torch.nn.functional.mish": [[1719, "torch-nn-functional-mish"]], "torch.nn.functional.relu_": [[1736, "torch-nn-functional-relu"]], "torch.nn.functional.one_hot": [[1726, "torch-nn-functional-one-hot"]], "torch.nn.functional.prelu": [[1733, "torch-nn-functional-prelu"]], "torch.nn.functional.smooth_l1_loss": [[1744, "torch-nn-functional-smooth-l1-loss"]], "torch.nn.functional.upsample_bilinear": [[1760, "torch-nn-functional-upsample-bilinear"]], "torch.nn.functional.scaled_dot_product_attention": [[1740, "torch-nn-functional-scaled-dot-product-attention"]], "torch.nn.functional.tanhshrink": [[1752, "torch-nn-functional-tanhshrink"]], "torch.nn.functional.triplet_margin_loss": [[1756, "torch-nn-functional-triplet-margin-loss"]], "torch.nn.functional.unfold": [[1758, "torch-nn-functional-unfold"]], "torch.nn.functional.multilabel_soft_margin_loss": [[1723, "torch-nn-functional-multilabel-soft-margin-loss"]], "torch.nn.functional.threshold": [[1753, "torch-nn-functional-threshold"]], "torch.nn.functional.max_unpool2d": [[1717, "torch-nn-functional-max-unpool2d"]], "torch.nn.functional.softmin": [[1747, "torch-nn-functional-softmin"]], "torch.nn.functional.pdist": [[1729, "torch-nn-functional-pdist"]], "torch.nn.functional.nll_loss": [[1724, "torch-nn-functional-nll-loss"]], "torch.nn.modules.module.register_module_buffer_registration_hook": [[1764, "torch-nn-modules-module-register-module-buffer-registration-hook"]], "torch.nn.functional.max_unpool3d": [[1718, "torch-nn-functional-max-unpool3d"]], "torch.nn.functional.threshold_": [[1754, "torch-nn-functional-threshold"]], "torch.nn.functional.rrelu_": [[1739, "torch-nn-functional-rrelu"]], "torch.nn.functional.upsample": [[1759, "torch-nn-functional-upsample"]], "torch.nn.functional.softmax": [[1746, "torch-nn-functional-softmax"]], "torch.nn.functional.mse_loss": [[1720, "torch-nn-functional-mse-loss"]], "torch.nn.functional.pixel_unshuffle": [[1731, "torch-nn-functional-pixel-unshuffle"]], "torch.nn.functional.relu6": [[1735, "torch-nn-functional-relu6"]], "torch.reshape": [[1918, "torch-reshape"]], "MultiplicativeLR": [[1874, "multiplicativelr"]], "torch.poisson": [[1888, "torch-poisson"]], "torch.repeat_interleave": [[1917, "torch-repeat-interleave"]], "torch.quantized_batch_norm": [[1899, "torch-quantized-batch-norm"]], "torch.rand_like": [[1905, "torch-rand-like"]], "torch.rot90": [[1923, "torch-rot90"]], "torch.permute": [[1886, "torch-permute"]], "torch.randint": [[1906, 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[[2182, null]], "HowTo for PyTorch Backend Vendors": [[2182, null]], "MPS backend": [[2145, "mps-backend"]], "torch.optim": [[2157, "module-torch.optim"]], "How to use an optimizer": [[2157, "how-to-use-an-optimizer"]], "Constructing it": [[2157, "constructing-it"]], "Per-parameter options": [[2157, "per-parameter-options"]], "Taking an optimization step": [[2157, "taking-an-optimization-step"]], "optimizer.step()": [[2157, "optimizer-step"]], "optimizer.step(closure)": [[2157, "optimizer-step-closure"]], "Base class": [[2157, "base-class"]], "Algorithms": [[2157, "algorithms"]], "How to adjust learning rate": [[2157, "how-to-adjust-learning-rate"]], "How to utilize named parameters to load optimizer state dict": [[2157, "how-to-utilize-named-parameters-to-load-optimizer-state-dict"]], "Weight Averaging (SWA and EMA)": [[2157, "weight-averaging-swa-and-ema"]], "Constructing averaged models": [[2157, "constructing-averaged-models"]], "Custom averaging strategies": [[2157, 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"Construction of sparse compressed tensors": [[2170, "construction-of-sparse-compressed-tensors"]], "Supported operations": [[2170, "supported-operations"]], "Linear Algebra operations": [[2170, "linear-algebra-operations"]], "Tensor methods and sparse": [[2170, "tensor-methods-and-sparse"]], "Torch functions specific to sparse Tensors": [[2170, "torch-functions-specific-to-sparse-tensors"]], "Other functions": [[2170, "other-functions"]], "Zero-preserving unary functions": [[2170, "zero-preserving-unary-functions"]], "Example: AlexNet from PyTorch to ONNX": [[2155, "example-alexnet-from-pytorch-to-onnx"]], "Tracing vs Scripting": [[2155, "tracing-vs-scripting"]], "Avoiding Pitfalls": [[2155, "avoiding-pitfalls"]], "Avoid NumPy and built-in Python types": [[2155, "avoid-numpy-and-built-in-python-types"]], "Avoid Tensor.data": [[2155, "avoid-tensor-data"]], "Avoid in-place operations when using tensor.shape in tracing mode": [[2155, 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[[2149, "serializing-torch-nn-modules-and-loading-them-in-c"]], "Saving and loading ScriptModules across PyTorch versions": [[2149, "saving-and-loading-scriptmodules-across-pytorch-versions"]], "torch.div performing integer division": [[2149, "torch-div-performing-integer-division"]], "torch.full always inferring a float dtype": [[2149, "torch-full-always-inferring-a-float-dtype"]], "Config": [[2149, "module-torch.utils.serialization"]], "CPU threading and TorchScript inference": [[2132, "cpu-threading-and-torchscript-inference"]], "Build options": [[2132, "build-options"]], "Runtime API": [[2132, "runtime-api"]], "Tuning the number of threads": [[2132, "tuning-the-number-of-threads"]], "CUDA semantics": [[2133, "cuda-semantics"]], "TensorFloat-32 (TF32) on Ampere (and later) devices": [[2133, "tensorfloat-32-tf32-on-ampere-and-later-devices"]], "Reduced Precision Reduction in FP16 GEMMs": [[2133, "reduced-precision-reduction-in-fp16-gemms"]], "Reduced Precision Reduction in BF16 GEMMs": [[2133, "reduced-precision-reduction-in-bf16-gemms"]], "Asynchronous execution": [[2133, "asynchronous-execution"]], "CUDA streams": [[2133, "cuda-streams"]], "Stream semantics of backward passes": [[2133, "stream-semantics-of-backward-passes"]], "BC note: Using grads on the default stream": [[2133, "bc-note-using-grads-on-the-default-stream"]], "Optimizing memory usage  with PYTORCH_CUDA_ALLOC_CONF": [[2133, "optimizing-memory-usage-with-pytorch-cuda-alloc-conf"]], "Using custom memory allocators for CUDA": [[2133, "using-custom-memory-allocators-for-cuda"]], "cuBLAS workspaces": [[2133, "cublas-workspaces"]], "cuFFT plan cache": [[2133, "cufft-plan-cache"]], "Just-in-Time Compilation": [[2133, "just-in-time-compilation"]], "Best practices": [[2133, "best-practices"]], "Device-agnostic code": [[2133, "device-agnostic-code"]], "Use pinned memory buffers": [[2133, "use-pinned-memory-buffers"]], "Use nn.parallel.DistributedDataParallel instead of multiprocessing or 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"Packaging your first model": [[2158, "packaging-your-first-model"]], "How do I\u2026": [[2158, "how-do-i"]], "See what is inside a package?": [[2158, "see-what-is-inside-a-package"]], "Treat the package like a ZIP archive": [[2158, "treat-the-package-like-a-zip-archive"]], "Use the file_structure() API": [[2158, "use-the-file-structure-api"]], "See why a given module was included as a dependency?": [[2158, "see-why-a-given-module-was-included-as-a-dependency"]], "Include arbitrary resources with my package and access them later?": [[2158, "include-arbitrary-resources-with-my-package-and-access-them-later"]], "Customize how a class is packaged?": [[2158, "customize-how-a-class-is-packaged"]], "Test in my source code whether or not it is executing inside a package?": [[2158, "test-in-my-source-code-whether-or-not-it-is-executing-inside-a-package"]], "Patch code into a package?": [[2158, "patch-code-into-a-package"]], "Access package contents from packaged code?": [[2158, "access-package-contents-from-packaged-code"]], "Distinguish between packaged code and non-packaged code?": [[2158, "distinguish-between-packaged-code-and-non-packaged-code"]], "Re-export an imported object?": [[2158, "re-export-an-imported-object"]], "Package a TorchScript module?": [[2158, "package-a-torchscript-module"]], "Explanation": [[2158, "explanation"]], "torch.package Format Overview": [[2158, "torch-package-format-overview"]], "Framework files": [[2158, "framework-files"]], "User files": [[2158, "user-files"]], "How torch.package finds your code\u2019s dependencies": [[2158, "how-torch-package-finds-your-code-s-dependencies"]], "Analyzing an object\u2019s dependencies": [[2158, "analyzing-an-object-s-dependencies"]], "Analyzing a module\u2019s dependencies": [[2158, "analyzing-a-module-s-dependencies"]], "Dependency Management": [[2158, "dependency-management"]], "intern": [[2158, "intern"]], "extern": [[2158, "extern"]], "mock": [[2158, "mock"]], "Refactoring": [[2158, 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@@ -718,52 +718,52 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 expect many use cases to require operating at the Storage level!</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.UntypedStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">UntypedStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#UntypedStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">UntypedStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#UntypedStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to bfloat16 type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.bool">
-<span class="sig-name descname"><span class="pre">bool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.bool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to bool type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.byte">
-<span class="sig-name descname"><span class="pre">byte</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.byte" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">byte</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.byte" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to byte type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.byteswap">
-<span class="sig-name descname"><span class="pre">byteswap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.byteswap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">byteswap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.byteswap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Swap bytes in underlying data.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.char">
-<span class="sig-name descname"><span class="pre">char</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.char" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">char</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.char" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to char type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a copy of this storage.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.complex_double">
-<span class="sig-name descname"><span class="pre">complex_double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.complex_double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">complex_double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.complex_double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to complex double type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.complex_float">
-<span class="sig-name descname"><span class="pre">complex_float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.complex_float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">complex_float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.complex_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to complex float type.</p>
 </dd></dl>
 
@@ -774,13 +774,13 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a CPU copy of this storage if it’s not already on the CPU.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in CUDA memory.</p>
 <p>If this object is already in CUDA memory and on the correct device, then
 no copy is performed and the original object is returned.</p>
@@ -811,7 +811,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to double type.</p>
 </dd></dl>
 
@@ -835,31 +835,31 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float8_e4m3fn">
-<span class="sig-name descname"><span class="pre">float8_e4m3fn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e4m3fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e4m3fn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e4m3fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e4m3fn type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float8_e4m3fnuz">
-<span class="sig-name descname"><span class="pre">float8_e4m3fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e4m3fnuz" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e4m3fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e4m3fnuz" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e4m3fnuz type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float8_e5m2">
-<span class="sig-name descname"><span class="pre">float8_e5m2</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e5m2" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e5m2</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e5m2" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e5m2 type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float8_e5m2fnuz">
-<span class="sig-name descname"><span class="pre">float8_e5m2fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e5m2fnuz" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e5m2fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e5m2fnuz" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e5m2fnuz type</p>
 </dd></dl>
 
@@ -893,7 +893,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.get_device">
-<span class="sig-name descname"><span class="pre">get_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.get_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.get_device" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
@@ -903,13 +903,13 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to half type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.hpu">
-<span class="sig-name descname"><span class="pre">hpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.hpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">hpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.hpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in HPU memory.</p>
 <p>If this object is already in HPU memory and on the correct device, then
 no copy is performed and the original object is returned.</p>
@@ -930,7 +930,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.int">
-<span class="sig-name descname"><span class="pre">int</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">int</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to int type.</p>
 </dd></dl>
 
@@ -946,7 +946,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.is_pinned">
-<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.is_pinned" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.is_pinned" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine whether the CPU storage is already pinned on device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -976,13 +976,13 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.long">
-<span class="sig-name descname"><span class="pre">long</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L297"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.long" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">long</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L297"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.long" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to long type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.mps">
-<span class="sig-name descname"><span class="pre">mps</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.mps" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mps</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.mps" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a MPS copy of this storage if it’s not already on the MPS.</p>
 </dd></dl>
 
@@ -998,7 +998,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.pin_memory">
-<span class="sig-name descname"><span class="pre">pin_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.pin_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pin_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.pin_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy the CPU storage to pinned memory, if it’s not already pinned.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1023,7 +1023,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.share_memory_">
-<span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#UntypedStorage.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.share_memory_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#UntypedStorage.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.share_memory_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Moves the storage to shared memory.</p>
 <p>This is a no-op for storages already in shared memory and for CUDA
 storages, which do not need to be moved for sharing across processes.
@@ -1058,13 +1058,13 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.short">
-<span class="sig-name descname"><span class="pre">short</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L305"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.short" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">short</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L305"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.short" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to short type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.size">
-<span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.size" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
@@ -1074,20 +1074,20 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.to" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.tolist">
-<span class="sig-name descname"><span class="pre">tolist</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.tolist" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">tolist</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.tolist" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list containing the elements of this storage.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.type" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><em>Union</em></a>[<em>_StorageBase</em>, <a class="reference internal" href="#torch.TypedStorage" title="torch.storage.TypedStorage"><em>TypedStorage</em></a>]</p>
@@ -1097,7 +1097,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.untyped">
-<span class="sig-name descname"><span class="pre">untyped</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.untyped" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">untyped</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.untyped" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
@@ -1132,67 +1132,67 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 which stores all of the data that the <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> views.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.TypedStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">TypedStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">TypedStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.bfloat16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.bfloat16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to bfloat16 type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.bool">
-<span class="sig-name descname"><span class="pre">bool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1352"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1352"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.bool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to bool type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.byte">
-<span class="sig-name descname"><span class="pre">byte</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.byte"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.byte" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">byte</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.byte"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.byte" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to byte type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.char">
-<span class="sig-name descname"><span class="pre">char</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.char"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.char" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">char</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.char"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.char" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to char type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a copy of this storage.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.complex_double">
-<span class="sig-name descname"><span class="pre">complex_double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.complex_double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1362"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.complex_double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">complex_double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.complex_double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1362"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.complex_double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to complex double type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.complex_float">
-<span class="sig-name descname"><span class="pre">complex_float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.complex_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.complex_float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">complex_float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.complex_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.complex_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to complex float type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.copy_">
-<span class="sig-name descname"><span class="pre">copy_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.copy_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.copy_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">copy_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.copy_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.copy_" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a CPU copy of this storage if it’s not already on the CPU.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.cuda"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1044"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.cuda"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1044"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in CUDA memory.</p>
 <p>If this object is already in CUDA memory and on the correct device, then
 no copy is performed and the original object is returned.</p>
@@ -1213,7 +1213,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.data_ptr">
-<span class="sig-name descname"><span class="pre">data_ptr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.data_ptr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.data_ptr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">data_ptr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.data_ptr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.data_ptr" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1223,7 +1223,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to double type.</p>
 </dd></dl>
 
@@ -1234,7 +1234,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.element_size">
-<span class="sig-name descname"><span class="pre">element_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.element_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1087"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.element_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">element_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.element_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1087"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.element_size" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1246,47 +1246,47 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.fill_">
-<span class="sig-name descname"><span class="pre">fill_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.fill_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.fill_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fill_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.fill_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.fill_" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float8_e4m3fn">
-<span class="sig-name descname"><span class="pre">float8_e4m3fn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e4m3fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e4m3fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e4m3fn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e4m3fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e4m3fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e4m3fn type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float8_e4m3fnuz">
-<span class="sig-name descname"><span class="pre">float8_e4m3fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e4m3fnuz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e4m3fnuz" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e4m3fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e4m3fnuz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e4m3fnuz" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e4m3fnuz type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float8_e5m2">
-<span class="sig-name descname"><span class="pre">float8_e5m2</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e5m2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e5m2" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e5m2</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e5m2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e5m2" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e5m2 type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float8_e5m2fnuz">
-<span class="sig-name descname"><span class="pre">float8_e5m2fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e5m2fnuz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1382"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e5m2fnuz" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e5m2fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e5m2fnuz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1382"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e5m2fnuz" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e5m2fnuz type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.from_buffer">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.from_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.from_buffer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.from_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.from_buffer" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.from_file">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Storage</span></span></span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.from_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.from_file" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Storage</span></span></span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.from_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.from_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a CPU storage backed by a memory-mapped file.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">shared</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then memory is shared between all processes.
 All changes are written to the file. If <code class="docutils literal notranslate"><span class="pre">shared</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code>, then the changes on
@@ -1309,7 +1309,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.get_device">
-<span class="sig-name descname"><span class="pre">get_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.get_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.get_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.get_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.get_device" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
@@ -1319,13 +1319,13 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.half"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.half"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to half type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.hpu">
-<span class="sig-name descname"><span class="pre">hpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.hpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1057"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.hpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">hpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.hpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1057"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.hpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in HPU memory.</p>
 <p>If this object is already in HPU memory and on the correct device, then
 no copy is performed and the original object is returned.</p>
@@ -1346,7 +1346,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.int">
-<span class="sig-name descname"><span class="pre">int</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">int</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to int type.</p>
 </dd></dl>
 
@@ -1362,7 +1362,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.is_pinned">
-<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.is_pinned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.is_pinned" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.is_pinned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.is_pinned" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine whether the CPU TypedStorage is already pinned on device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1377,7 +1377,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.is_shared">
-<span class="sig-name descname"><span class="pre">is_shared</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.is_shared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.is_shared" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_shared</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.is_shared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.is_shared" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1387,23 +1387,23 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.long">
-<span class="sig-name descname"><span class="pre">long</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.long"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.long" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">long</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.long"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.long" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to long type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.nbytes">
-<span class="sig-name descname"><span class="pre">nbytes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.nbytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1019"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.nbytes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">nbytes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.nbytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1019"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.nbytes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.pickle_storage_type">
-<span class="sig-name descname"><span class="pre">pickle_storage_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.pickle_storage_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.pickle_storage_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pickle_storage_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.pickle_storage_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.pickle_storage_type" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.pin_memory">
-<span class="sig-name descname"><span class="pre">pin_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.pin_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.pin_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pin_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.pin_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.pin_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy the CPU TypedStorage to pinned memory, if it’s not already pinned.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1418,34 +1418,34 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.resizable">
-<span class="sig-name descname"><span class="pre">resizable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.resizable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.resizable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">resizable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.resizable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.resizable" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.resize_">
-<span class="sig-name descname"><span class="pre">resize_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.resize_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.resize_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">resize_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.resize_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.resize_" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.share_memory_">
-<span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.share_memory_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.share_memory_" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="#torch.UntypedStorage.share_memory_" title="torch.UntypedStorage.share_memory_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.UntypedStorage.share_memory_()</span></code></a></p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.short">
-<span class="sig-name descname"><span class="pre">short</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.short"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.short" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">short</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.short"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.short" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to short type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.size">
-<span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.size" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in device memory.</p>
 <p>If this object is already on the correct device, then no copy is performed
 and the original object is returned.</p>
@@ -1466,13 +1466,13 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.tolist">
-<span class="sig-name descname"><span class="pre">tolist</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.tolist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.tolist" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">tolist</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.tolist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.tolist" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list containing the elements of this storage.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the type if <cite>dtype</cite> is not provided, else casts this object to
 the specified type.</p>
 <p>If this is already of the correct type, no copy is performed and the
@@ -1497,7 +1497,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.untyped">
-<span class="sig-name descname"><span class="pre">untyped</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.untyped"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/storage.py#L876"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.untyped" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">untyped</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.untyped"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L876"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.untyped" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the internal <a class="reference internal" href="#torch.UntypedStorage" title="torch.UntypedStorage"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.UntypedStorage</span></code></a>.</p>
 </dd></dl>
 
@@ -1505,7 +1505,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.DoubleStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">DoubleStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#DoubleStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.DoubleStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">DoubleStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#DoubleStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.DoubleStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1517,7 +1517,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.FloatStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">FloatStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#FloatStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.FloatStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">FloatStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#FloatStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.FloatStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1529,7 +1529,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.HalfStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">HalfStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#HalfStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.HalfStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">HalfStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#HalfStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.HalfStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1541,7 +1541,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.LongStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">LongStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#LongStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1864"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.LongStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">LongStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#LongStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1864"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.LongStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1553,7 +1553,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.IntStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">IntStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#IntStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1875"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.IntStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">IntStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#IntStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1875"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.IntStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1565,7 +1565,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ShortStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ShortStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ShortStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1886"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ShortStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ShortStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ShortStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1886"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ShortStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1577,7 +1577,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.CharStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">CharStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#CharStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.CharStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">CharStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#CharStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.CharStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1589,7 +1589,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ByteStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ByteStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ByteStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ByteStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ByteStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ByteStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ByteStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1601,7 +1601,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.BoolStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">BoolStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#BoolStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1908"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.BoolStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">BoolStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#BoolStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1908"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.BoolStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1613,7 +1613,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.BFloat16Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">BFloat16Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#BFloat16Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1919"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.BFloat16Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">BFloat16Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#BFloat16Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1919"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.BFloat16Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1625,7 +1625,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ComplexDoubleStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ComplexDoubleStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ComplexDoubleStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1930"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ComplexDoubleStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ComplexDoubleStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ComplexDoubleStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1930"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ComplexDoubleStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1637,7 +1637,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ComplexFloatStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ComplexFloatStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ComplexFloatStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ComplexFloatStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ComplexFloatStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ComplexFloatStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ComplexFloatStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1649,7 +1649,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QUInt8Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt8Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt8Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1952"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt8Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt8Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt8Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1952"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt8Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1661,7 +1661,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QInt8Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QInt8Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QInt8Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1963"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QInt8Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QInt8Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QInt8Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1963"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QInt8Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1673,7 +1673,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QInt32Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QInt32Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QInt32Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1974"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QInt32Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QInt32Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QInt32Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1974"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QInt32Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1685,7 +1685,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QUInt4x2Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt4x2Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt4x2Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1985"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt4x2Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt4x2Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt4x2Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1985"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt4x2Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1697,7 +1697,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QUInt2x4Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt2x4Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt2x4Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L1996"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt2x4Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt2x4Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt2x4Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1996"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt2x4Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
diff --git a/main/tensor_attributes.html b/main/tensor_attributes.html
index ac3b50ea455..4d8a379bb06 100644
--- a/main/tensor_attributes.html
+++ b/main/tensor_attributes.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/tensor_view.html b/main/tensor_view.html
index f1937c2323a..2d90873f258 100644
--- a/main/tensor_view.html
+++ b/main/tensor_view.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/tensorboard.html b/main/tensorboard.html
index 76716f6c2e4..b0eafcd283e 100644
--- a/main/tensorboard.html
+++ b/main/tensorboard.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -669,7 +669,7 @@
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.tensorboard.writer.</span></span><span class="sig-name descname"><span class="pre">SummaryWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.tensorboard.writer.</span></span><span class="sig-name descname"><span class="pre">SummaryWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Writes entries directly to event files in the log_dir to be consumed by TensorBoard.</p>
 <p>The <cite>SummaryWriter</cite> class provides a high-level API to create an event file
 in a given directory and add summaries and events to it. The class updates the
@@ -678,7 +678,7 @@
 training.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <cite>SummaryWriter</cite> that will write out events and summaries to the event file.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -725,7 +725,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_scalar">
-<span class="sig-name descname"><span class="pre">add_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scalar_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">double_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalar" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scalar_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">double_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalar" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add scalar data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -755,7 +755,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_scalars">
-<span class="sig-name descname"><span class="pre">add_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">main_tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag_scalar_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalars" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">main_tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag_scalar_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalars" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add many scalar data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -787,7 +787,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_histogram">
-<span class="sig-name descname"><span class="pre">add_histogram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tensorflow'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_histogram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_histogram" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_histogram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tensorflow'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_histogram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_histogram" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add histogram to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -818,7 +818,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_image">
-<span class="sig-name descname"><span class="pre">add_image</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_image"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_image" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_image</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_image"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_image" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add image data to summary.</p>
 <p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">pillow</span></code> package.</p>
 <dl class="field-list simple">
@@ -866,7 +866,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_images">
-<span class="sig-name descname"><span class="pre">add_images</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'NCHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_images"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_images" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_images</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'NCHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_images"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_images" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add batched image data to summary.</p>
 <p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">pillow</span></code> package.</p>
 <dl class="field-list simple">
@@ -907,7 +907,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_figure">
-<span class="sig-name descname"><span class="pre">add_figure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">close</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_figure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L723"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_figure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_figure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">close</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_figure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L723"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_figure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Render matplotlib figure into an image and add it to summary.</p>
 <p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code> package.</p>
 <dl class="field-list simple">
@@ -926,7 +926,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_video">
-<span class="sig-name descname"><span class="pre">add_video</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vid_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_video"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L761"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_video" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_video</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vid_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_video"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L761"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_video" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add video data to summary.</p>
 <p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">moviepy</span></code> package.</p>
 <dl class="field-list simple">
@@ -949,7 +949,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_audio">
-<span class="sig-name descname"><span class="pre">add_audio</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snd_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">44100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_audio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L781"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_audio" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_audio</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snd_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">44100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_audio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L781"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_audio" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add audio data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -971,7 +971,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_text">
-<span class="sig-name descname"><span class="pre">add_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_text" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_text" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add text data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -993,7 +993,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_graph">
-<span class="sig-name descname"><span class="pre">add_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_to_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_strict_trace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_to_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_strict_trace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add graph data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1012,7 +1012,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_embedding">
-<span class="sig-name descname"><span class="pre">add_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_img</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_embedding" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_img</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_embedding" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add embedding projector data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1061,7 +1061,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve">
-<span class="sig-name descname"><span class="pre">add_pr_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_thresholds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">127</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_pr_curve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L958"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_pr_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_thresholds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">127</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_pr_curve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L958"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add precision recall curve.</p>
 <p>Plotting a precision-recall curve lets you understand your model’s
 performance under different threshold settings. With this function,
@@ -1096,7 +1096,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars">
-<span class="sig-name descname"><span class="pre">add_custom_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_custom_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L1094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_custom_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_custom_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L1094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create special chart by collecting charts tags in ‘scalars’.</p>
 <p>NOTE: This function can only be called once for each SummaryWriter() object.</p>
 <p>Because it only provides metadata to tensorboard, the function can be called before or after the training loop.</p>
@@ -1120,7 +1120,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_mesh">
-<span class="sig-name descname"><span class="pre">add_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vertices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">faces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L1118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_mesh" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vertices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">faces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L1118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_mesh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add meshes or 3D point clouds to TensorBoard.</p>
 <p>The visualization is based on Three.js,
 so it allows users to interact with the rendered object. Besides the basic definitions
@@ -1178,7 +1178,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_hparams">
-<span class="sig-name descname"><span class="pre">add_hparams</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hparam_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hparam_domain_discrete</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_hparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_hparams" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_hparams</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hparam_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hparam_domain_discrete</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_hparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_hparams" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a set of hyperparameters to be compared in TensorBoard.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1214,7 +1214,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.flush">
-<span class="sig-name descname"><span class="pre">flush</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.flush"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L1185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.flush" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">flush</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.flush"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L1185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.flush" title="Permalink to this definition">¶</a></dt>
 <dd><p>Flushes the event file to disk.</p>
 <p>Call this method to make sure that all pending events have been written to
 disk.</p>
@@ -1222,7 +1222,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.close">
-<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/utils/tensorboard/writer.py#L1196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.close" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L1196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.close" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
diff --git a/main/tensors.html b/main/tensors.html
index 97fa2683372..d3e574ea8cf 100644
--- a/main/tensors.html
+++ b/main/tensors.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/testing.html b/main/testing.html
index 6165a566a6b..9f5319333f8 100644
--- a/main/testing.html
+++ b/main/testing.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="torch-testing"></span><h1>torch.testing<a class="headerlink" href="#module-torch.testing" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.testing.assert_close">
-<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">assert_close</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_subclasses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equal_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_comparison.html#assert_close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/testing/_comparison.py#L1249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.assert_close" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">assert_close</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_subclasses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equal_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_comparison.html#assert_close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/testing/_comparison.py#L1249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.assert_close" title="Permalink to this definition">¶</a></dt>
 <dd><p>Asserts that <code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> are close.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> are strided, non-quantized, real-valued, and finite, they are considered close if</p>
 <div class="math">
@@ -882,7 +882,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.testing.make_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">make_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noncontiguous</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_creation.html#make_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/testing/_creation.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.make_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">make_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noncontiguous</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_creation.html#make_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/testing/_creation.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.make_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a tensor with the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">shape</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code>, and <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>, and filled with
 values uniformly drawn from <code class="docutils literal notranslate"><span class="pre">[low,</span> <span class="pre">high)</span></code>.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">low</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">high</span></code> are specified and are outside the range of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>’s representable
@@ -977,7 +977,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.testing.assert_allclose">
-<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">assert_allclose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equal_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_comparison.html#assert_allclose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/testing/_comparison.py#L1522"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.assert_allclose" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">assert_allclose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equal_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_comparison.html#assert_allclose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/testing/_comparison.py#L1522"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.assert_allclose" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p><a class="reference internal" href="#torch.testing.assert_allclose" title="torch.testing.assert_allclose"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.testing.assert_allclose()</span></code></a> is deprecated since <code class="docutils literal notranslate"><span class="pre">1.12</span></code> and will be removed in a future release.
diff --git a/main/threading_environment_variables.html b/main/threading_environment_variables.html
index f6e9ffaed23..484e537576e 100644
--- a/main/threading_environment_variables.html
+++ b/main/threading_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.ao.ns._numeric_suite.html b/main/torch.ao.ns._numeric_suite.html
index bf53f84709f..b8df030f3a3 100644
--- a/main/torch.ao.ns._numeric_suite.html
+++ b/main/torch.ao.ns._numeric_suite.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -618,7 +618,7 @@
 </div>
 <span class="target" id="module-torch.ao.ns._numeric_suite"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.compare_weights">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantized_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantized_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compare the weights of the float module with its corresponding quantized
 module. Return a dict with key corresponding to module names and each entry being
 a dictionary with two keys ‘float’ and ‘quantized’, containing the float and
@@ -657,7 +657,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.get_logger_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">get_logger_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_logger_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_logger_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">get_logger_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_logger_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_logger_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Traverse the modules and save all logger stats into target dict.
 This is mainly used for quantization accuracy debug.</p>
 <dl class="simple">
@@ -683,41 +683,41 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Logger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">Logger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">Logger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for stats logging</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Logger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.ShadowLogger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">ShadowLogger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">ShadowLogger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Class used in Shadow module to record the outputs of the original and
 shadow modules.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.ShadowLogger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.OutputLogger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">OutputLogger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">OutputLogger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Class used to log the outputs of the module</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.OutputLogger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">Shadow</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">Shadow</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow" title="Permalink to this definition">¶</a></dt>
 <dd><p>Shadow module attaches the float module to its matching quantized module
 as the shadow. Then it uses Logger module to process the outputs of both
 modules.</p>
@@ -733,7 +733,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.forward" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -743,7 +743,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.add">
-<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -753,7 +753,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.add_scalar">
-<span class="sig-name descname"><span class="pre">add_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_scalar" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_scalar" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -763,7 +763,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.mul">
-<span class="sig-name descname"><span class="pre">mul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.mul"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.mul" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.mul"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.mul" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
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@@ -773,7 +773,7 @@
 
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 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.mul_scalar">
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+<span class="sig-name descname"><span class="pre">mul_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.mul_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.mul_scalar" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
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@@ -783,7 +783,7 @@
 
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 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.cat">
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+<span class="sig-name descname"><span class="pre">cat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.cat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.cat" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -793,7 +793,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.add_relu">
-<span class="sig-name descname"><span class="pre">add_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_relu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_relu" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -805,7 +805,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.prepare_model_with_stubs">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">prepare_model_with_stubs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_swap_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_with_stubs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_with_stubs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">prepare_model_with_stubs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_swap_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_with_stubs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_with_stubs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepare the model by attaching the float module to its matching quantized
 module as the shadow if the float module type is in module_swap_list.</p>
 <p>Example usage:</p>
@@ -829,7 +829,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.compare_model_stub">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_model_stub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_swap_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.ShadowLogger'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_stub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_stub" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_model_stub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_swap_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.ShadowLogger'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_stub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_stub" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compare quantized module in a model with its floating point counterpart,
 feeding both of them the same input. Return a dict with key corresponding to
 module names and each entry being a dictionary with two keys ‘float’ and
@@ -870,7 +870,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.get_matching_activations">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">get_matching_activations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_matching_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_matching_activations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">get_matching_activations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_matching_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_matching_activations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find the matching activation between float and quantized modules.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -892,7 +892,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.prepare_model_outputs">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">prepare_model_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_outputs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">prepare_model_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_outputs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepare the model by attaching the logger to both float module
 and quantized module if they are in the allow_list.</p>
 <dl class="field-list simple">
@@ -909,7 +909,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.compare_model_outputs">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_model_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_outputs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_model_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_outputs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compare output activations between float and quantized models at
 corresponding locations for the same input. Return a dict with key corresponding
 to quantized module names and each entry being a dictionary with two keys
diff --git a/main/torch.ao.ns._numeric_suite_fx.html b/main/torch.ao.ns._numeric_suite_fx.html
index e4d9ab33381..d4aa5fc616e 100644
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -701,39 +701,39 @@
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 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.OutputLogger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">OutputLogger</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_within_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_of_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fqn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_str</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputLogger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">OutputLogger</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_within_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_of_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fqn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_str</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputLogger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for capturing intermediate values.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.OutputLogger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputLogger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputLogger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.OutputComparisonLogger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">OutputComparisonLogger</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputComparisonLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputComparisonLogger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">OutputComparisonLogger</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputComparisonLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputComparisonLogger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Same as OutputLogger, but also requires the original activation
 in order to calculate the comparison at calibration time</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.OutputComparisonLogger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_ref</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputComparisonLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputComparisonLogger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_ref</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputComparisonLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputComparisonLogger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.NSTracer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">NSTracer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">skipped_module_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skipped_module_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">NSTracer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">skipped_module_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skipped_module_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Just like a regular FX quantization tracer, but treats observers and fake_quantize
 modules as leaf modules.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.NSTracer.is_leaf_module">
-<span class="sig-name descname"><span class="pre">is_leaf_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_qualified_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer.is_leaf_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer.is_leaf_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_leaf_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_qualified_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer.is_leaf_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer.is_leaf_module" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
@@ -745,7 +745,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extract_weights">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_to_type_to_weight_extraction_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_to_type_to_weight_extraction_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extract weights from model A and model B, and return a comparison.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -771,7 +771,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.add_loggers">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">add_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#add_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_loggers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">add_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#add_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_loggers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Instrument model A and model B with loggers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -796,7 +796,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extract_logger_info">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_logger_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_logger_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_logger_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_logger_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Traverse all loggers in <cite>model_a</cite> and <cite>model_b</cite>, and extract the logged
 information.</p>
 <dl class="field-list simple">
@@ -820,7 +820,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.add_shadow_loggers">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">add_shadow_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_type_to_io_type_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#add_shadow_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L701"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_shadow_loggers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">add_shadow_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_type_to_io_type_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#add_shadow_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L701"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_shadow_loggers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Instrument model A and model B with shadow loggers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -843,7 +843,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extract_shadow_logger_info">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_shadow_logger_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a_shadows_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_shadow_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_shadow_logger_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_shadow_logger_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a_shadows_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_shadow_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_shadow_logger_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Traverse all loggers in a shadow model, and extract the logged
 information.</p>
 <dl class="field-list simple">
@@ -866,7 +866,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extend_logger_results_with_comparison">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extend_logger_results_with_comparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extend_logger_results_with_comparison"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L790"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extend_logger_results_with_comparison" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extend_logger_results_with_comparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extend_logger_results_with_comparison"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L790"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extend_logger_results_with_comparison" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compares the logged values from <cite>model_name_2</cite> against the corresponding
 values in <cite>model_name_1</cite>, using <cite>comparison_fn</cite>. Records the result
 in <cite>model_name_2</cite>’s results under <cite>comparison_name</cite>. Modifies <cite>results</cite> inplace.</p>
@@ -887,7 +887,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.prepare_n_shadows_model">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_multi_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prepare_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prepare_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_tracer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#prepare_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.prepare_n_shadows_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_multi_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prepare_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prepare_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_tracer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#prepare_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.prepare_n_shadows_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a model with a graph with M ops such as</p>
 <blockquote>
 <div><p>args_kwargs_m -&gt; op_m -&gt; output_m</p>
@@ -921,7 +921,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.loggers_set_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">loggers_set_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#loggers_set_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L1063"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.loggers_set_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">loggers_set_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#loggers_set_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1063"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.loggers_set_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the <cite>enabled</cite> setting on a <cite>model</cite>’s loggers</p>
 <dl class="field-list simple">
 </dl>
@@ -929,7 +929,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.loggers_set_save_activations">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">loggers_set_save_activations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_activations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#loggers_set_save_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L1074"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.loggers_set_save_activations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">loggers_set_save_activations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_activations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#loggers_set_save_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1074"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.loggers_set_save_activations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the <cite>save_activations</cite> setting on a <cite>model</cite>’s loggers</p>
 <dl class="field-list simple">
 </dl>
@@ -937,7 +937,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.convert_n_shadows_model">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">convert_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_convert_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_convert_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#convert_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L1086"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.convert_n_shadows_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">convert_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_convert_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_convert_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#convert_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1086"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.convert_n_shadows_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a model from <cite>prepare_n_shadows_model</cite>, runs <cite>convert_fx</cite>
 on each shadow submodule.</p>
 <dl class="field-list simple">
@@ -949,7 +949,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extract_results_n_shadows_model">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_results_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_results_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L1111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_results_n_shadows_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_results_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_results_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_results_n_shadows_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extracts logger results from <cite>model</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -960,7 +960,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.print_comparisons_n_shadows_model">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">print_comparisons_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#print_comparisons_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/_numeric_suite_fx.py#L1120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.print_comparisons_n_shadows_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">print_comparisons_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#print_comparisons_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.print_comparisons_n_shadows_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prints a summary of extracted <cite>results</cite>.</p>
 <dl class="field-list simple">
 </dl>
@@ -975,17 +975,17 @@ <h1>torch.ao.ns.fx.utils<a class="headerlink" href="#torch-ao-ns-fx-utils" title
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-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_sqnr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_sqnr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_sqnr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_sqnr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_sqnr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_sqnr" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
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-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_normalized_l2_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_normalized_l2_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_normalized_l2_error" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_normalized_l2_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_normalized_l2_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_normalized_l2_error" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns.fx.utils.compute_cosine_similarity">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_cosine_similarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_cosine_similarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_cosine_similarity" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_cosine_similarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_cosine_similarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_cosine_similarity" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/torch.compiler_performance_dashboard.html b/main/torch.compiler_performance_dashboard.html
index 73731a7e7c3..f447a4000e5 100644
--- a/main/torch.compiler_performance_dashboard.html
+++ b/main/torch.compiler_performance_dashboard.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.compiler_profiling_torch_compile.html b/main/torch.compiler_profiling_torch_compile.html
index df74efa0a1d..1edf9ac3c98 100644
--- a/main/torch.compiler_profiling_torch_compile.html
+++ b/main/torch.compiler_profiling_torch_compile.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.compiler_transformations.html b/main/torch.compiler_transformations.html
index 5c5e542b0b6..f9dfdf3667b 100644
--- a/main/torch.compiler_transformations.html
+++ b/main/torch.compiler_transformations.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.compiler_troubleshooting.html b/main/torch.compiler_troubleshooting.html
index 78100052ecf..0a46db1ee65 100644
--- a/main/torch.compiler_troubleshooting.html
+++ b/main/torch.compiler_troubleshooting.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.compiler_troubleshooting_old.html b/main/torch.compiler_troubleshooting_old.html
index 5eb1c573e5d..e6116c9b84a 100644
--- a/main/torch.compiler_troubleshooting_old.html
+++ b/main/torch.compiler_troubleshooting_old.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.html b/main/torch.html
index 0060e6a1dca..8bd253406f0 100644
--- a/main/torch.html
+++ b/main/torch.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -2257,13 +2257,13 @@ <h2>Utilities<a class="headerlink" href="#utilities" title="Permalink to this he
 <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.SymInt">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymInt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymInt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymInt" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymInt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymInt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymInt" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like an int (including magic methods), but redirects all operations on the
 wrapped node. This is used in particular to symbolically record operations
 in the symbolic shape workflow.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymInt.as_integer_ratio">
-<span class="sig-name descname"><span class="pre">as_integer_ratio</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymInt.as_integer_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L586"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymInt.as_integer_ratio" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">as_integer_ratio</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymInt.as_integer_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L586"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymInt.as_integer_ratio" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent this int as an exact integer ratio</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2276,13 +2276,13 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.SymFloat">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymFloat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymFloat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like an float (including magic methods), but redirects all operations on the
 wrapped node. This is used in particular to symbolically record operations
 in the symbolic shape workflow.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymFloat.as_integer_ratio">
-<span class="sig-name descname"><span class="pre">as_integer_ratio</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.as_integer_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.as_integer_ratio" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">as_integer_ratio</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.as_integer_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.as_integer_ratio" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent this float as an exact integer ratio</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2293,7 +2293,7 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymFloat.conjugate">
-<span class="sig-name descname"><span class="pre">conjugate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.conjugate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">conjugate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.conjugate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the complex conjugate of the float.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2304,7 +2304,7 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymFloat.hex">
-<span class="sig-name descname"><span class="pre">hex</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.hex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L715"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.hex" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">hex</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.hex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L715"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.hex" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the hexadecimal representation of the float.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2315,7 +2315,7 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymFloat.is_integer">
-<span class="sig-name descname"><span class="pre">is_integer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.is_integer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L694"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.is_integer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_integer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.is_integer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L694"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.is_integer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if the float is an integer.</p>
 </dd></dl>
 
@@ -2323,7 +2323,7 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.SymBool">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymBool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymBool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/__init__.py#L720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymBool" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymBool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymBool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymBool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like an bool (including magic methods), but redirects all operations on the
 wrapped node. This is used in particular to symbolically record operations
 in the symbolic shape workflow.</p>
diff --git a/main/torch.overrides.html b/main/torch.overrides.html
index 85cc5e841e7..d4c2da78184 100644
--- a/main/torch.overrides.html
+++ b/main/torch.overrides.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -615,7 +615,7 @@
 <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.get_ignored_functions">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_ignored_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_ignored_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/overrides.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_ignored_functions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_ignored_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_ignored_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_ignored_functions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return public functions that cannot be overridden by <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -638,7 +638,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.get_overridable_functions">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_overridable_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_overridable_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/overrides.py#L1894"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_overridable_functions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_overridable_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_overridable_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1894"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_overridable_functions" title="Permalink to this definition">¶</a></dt>
 <dd><p>List functions that are overridable via __torch_function__</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -653,7 +653,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.resolve_name">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">resolve_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#resolve_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/overrides.py#L1907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.resolve_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">resolve_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#resolve_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.resolve_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get a human readable string name for a function passed to
 __torch_function__</p>
 <dl class="field-list simple">
@@ -672,7 +672,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.get_testing_overrides">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_testing_overrides</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_testing_overrides"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/overrides.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_testing_overrides" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_testing_overrides</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_testing_overrides"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_testing_overrides" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dict containing dummy overrides for all overridable functions</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -696,7 +696,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.handle_torch_function">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">handle_torch_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">public_api</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relevant_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#handle_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/overrides.py#L1670"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.handle_torch_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">handle_torch_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">public_api</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relevant_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#handle_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1670"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.handle_torch_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement a function with checks for <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code> overrides.</p>
 <p>See torch::autograd::handle_torch_function for the equivalent of this
 function in the C++ implementation.</p>
@@ -759,7 +759,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.is_tensor_like">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">is_tensor_like</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inp</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_like"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/overrides.py#L1962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_like" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">is_tensor_like</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inp</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_like"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_like" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if the passed-in input is a Tensor-like.</p>
 <p>Currently, this occurs whenever there’s a <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>
 attribute on the type of the input.</p>
@@ -793,7 +793,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.is_tensor_method_or_property">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">is_tensor_method_or_property</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_method_or_property"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/overrides.py#L1936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_method_or_property" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">is_tensor_method_or_property</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_method_or_property"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_method_or_property" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if the function passed in is a handler for a
 method or property belonging to <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, as passed
 into <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>.</p>
@@ -823,7 +823,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.wrap_torch_function">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">wrap_torch_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dispatcher</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#wrap_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/overrides.py#L1554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.wrap_torch_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">wrap_torch_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dispatcher</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#wrap_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.wrap_torch_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wraps a given function with <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code> -related functionality.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/torch_cuda_memory.html b/main/torch_cuda_memory.html
index bb1b02a3ce9..34f3c7bb192 100644
--- a/main/torch_cuda_memory.html
+++ b/main/torch_cuda_memory.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -654,7 +654,7 @@ <h2>Allocator State History<a class="headerlink" href="#allocator-state-history"
 <h1>Snapshot API Reference<a class="headerlink" href="#snapshot-api-reference" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory._record_memory_history">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_record_memory_history</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_entries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">9223372036854775807</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_record_memory_history"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._record_memory_history" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_record_memory_history</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_entries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">9223372036854775807</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_record_memory_history"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._record_memory_history" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable recording of stack traces associated with memory
 allocations, so you can tell what allocated any piece of memory in
 <a class="reference internal" href="#torch.cuda.memory._snapshot" title="torch.cuda.memory._snapshot"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cuda.memory._snapshot()</span></code></a>.</p>
@@ -691,7 +691,7 @@ <h1>Snapshot API Reference<a class="headerlink" href="#snapshot-api-reference" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory._snapshot">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_snapshot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_snapshot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save a snapshot of CUDA memory state at the time it was called.</p>
 <p>The state is represented as a dictionary with the following structure.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Snapshot</span><span class="p">(</span><span class="n">TypedDict</span><span class="p">):</span>
@@ -768,7 +768,7 @@ <h1>Snapshot API Reference<a class="headerlink" href="#snapshot-api-reference" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory._dump_snapshot">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_dump_snapshot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dump_snapshot.pickle'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_dump_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/2f24f2eb46102381430777af2985b6af6e5d2cec/torch/cuda/memory.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._dump_snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_dump_snapshot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dump_snapshot.pickle'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_dump_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._dump_snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save a pickled version of the <cite>torch.memory._snapshot()</cite> dictionary to a file.</p>
 <p>This file can be opened by the interactive snapshot viewer at pytorch.org/memory_viz</p>
 <dl class="field-list simple">
diff --git a/main/torch_environment_variables.html b/main/torch_environment_variables.html
index 8651555fdd7..b801056e263 100644
--- a/main/torch_environment_variables.html
+++ b/main/torch_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch_nccl_environment_variables.html b/main/torch_nccl_environment_variables.html
index 3deb9f4ace0..6954200a348 100644
--- a/main/torch_nccl_environment_variables.html
+++ b/main/torch_nccl_environment_variables.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git2f24f2e ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/type_info.html b/main/type_info.html
index 2da1e4b965a..42fe9986e32 100644
--- a/main/type_info.html
+++ b/main/type_info.html
@@ -300,7 +300,7 @@
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